F5 DPU ROI Calculator | Enterprise Analytics Suite v4.9

🔄 How F5 DPUs Unlock GPU Potential

▼

TRADITIONAL (Bottleneck)

Traffic

→

CPU

OVERLOAD

- - →

GPU

Waiting...

Low Util.

WITH F5 DPU (Optimized)

Traffic

→

DPU

↓ Network Offload

→

CPU

App Logic

→

GPU

AI WORK

Max Util.

The "CPU Tax" Problem: In traditional architectures, CPUs handle networking and data tasks, leaving expensive GPUs idle. F5 DPUs offload this overhead, creating a "fast lane" that liberates CPUs and unlocks GPU potential for higher utilization and throughput.

🔧 CPU Offload Breakdown ▼

Model-weighted overhead: 38% → F5 recovers: 22%

CPU Overhead by Function

Offloaded to DPU

Overhead on CPU

✓ Full ◐ Partial ○ None

Why Larger Models Benefit More

Key Insight: Larger models have proportionally more CPU-bound operations (especially KV cache management), which is why F5 DPU ROI increases with model size.

📋 View Detailed Function Breakdown

Function	CPU Overhead	F5 Offload	Notes
KV Cache Management	15-25%	✓ Full	Grows with context length; biggest gain for large models
Network I/O & Protocols	10-15%	✓ Full	TCP/IP stack, gRPC/REST parsing, response assembly
SSL/TLS Processing	8-12%	✓ Full	Encryption/decryption, certificate validation
Memory & DMA Operations	8-12%	◐ Partial	Buffer management, data movement
Request Batching & Scheduling	5-10%	◐ Partial	Dynamic batching decisions, queue management
Load Balancing & Security	5-10%	✓ Full	Request routing, health checks, WAF
Tokenization & Telemetry	3-8%	○ None	Pre/post processing, metrics, logging

⚙️ How CPU Offload Drives GPU Utilization MECHANISM

▼

CPU Time Allocation

Before F5

🔴 38% overhead 🔵 62% app logic

With F5 on the DPU (offloads overhead)

🔴 16% overhead 🟢 22% freed 🔵 62% app

→

ENABLES

GPU Utilization Impact

42%

Baseline

58%

With F5

+16pp uplift (+38% relative)

→

DRIVES

Business Outcome

Throughput Gain

+57%

tokens/sec increase

Equivalent GPUs Freed

capacity reclaimed

Revenue Density Uplift

+0%

$/GPU/year improvement

💡

This is the mechanism, not a separate benefit. CPU overhead reduction is how F5 delivers GPU utilization improvement — it's not counted separately in the ROI. The freed CPU cycles remove the bottleneck that kept GPUs waiting, enabling higher utilization and throughput, which flow into the financial metrics below.

🔀 How GPU-Aware Load Balancing Drives Token Revenue (Stream B) MECHANISM

▼

Traditional (Round-Robin)

HAProxy / NGINX

Round-robin routing

↓ ↓ ↓

GPU 1

98% busy

GPU 2

30% busy

GPU 3

95% busy

Hotspots

GPUs overloaded

Requests Drop

Queue overflow

High TTFT

Slow first token

With F5 DPU (GPU-Aware)

F5 BIG-IP Next on DPU

GPU-aware routing + KV cache state

↓ balanced ↓ balanced ↓ balanced

GPU 1

75% busy

GPU 2

72% busy

GPU 3

74% busy

+40%

Throughput

−61%

TTFT Latency

Annual Revenue

💡

This is token revenue, not a separate value-add. GPU-aware routing steers requests to the least-loaded GPU, eliminating hotspots and queue overflow. The result: more tokens served per second = more revenue. This is included in the Token Revenue column in the cash flow table. Tolly Report #226104 measured 21–406% throughput improvement depending on model size.

Annual ROI

Return on Investment

NPV (Discounted)

3-year @ 12% WACC

Payback Period ⓘ

0 mo

F5 Cost ÷ Net Benefit

IRR

vs 12% hurdle

F5 Token Revenue

per year (all tokens)

Customer Token Revenue

per year (all tokens)

F5 Share of Revenue

F5 price as % of customer price

Token Throughput (with F5)

tokens/sec fleet total

🎯

GPU Efficiency Frontier — Before vs After F5

Cost-per-GPU-Hour vs Throughput-per-GPU across GPU generations

JENSEN GTC STYLE ▼

Compare optimization layers: F5 BNK on DPU Tolly-certified NVIDIA Dynamo NVIDIA-reported vLLM (PagedAttention) Community benchmarks

⚡ Cost vs Throughput — higher is better

🔮 What-If: ROI with KV Cache Optimization EXPLORATORY

Include KV Cache

Stream A: CPU Offload Tolly Validated

Annual value from DPU-based load balancing freeing host CPU cores

Cores Freed: 0

CPU Reduction: 83%

Workload Value: $0

Power Saved: $0

Stream B: AI Inference LB Tolly Validated

Annual value from GPU-aware traffic steering — more tokens, lower latency

Throughput Boost: 0%

TTFT Improvement: 0%

Token Revenue: $0

Latency Value: $0

📊 Value Stream Comparison

📊 TCO Bridge Analysis (3-Year) ▼

Category	Without F5	With F5	Delta
Total TCO

📐

How ROI is Calculated 20% Realization Core

              ROI = (Token Revenue + OpEx Savings − Power − F5 Cost) ÷ F5 Cost
            

Token Revenue: Δ revenue from F5 throughput gain (baseline = $0)

GPU Capacity: Freed GPUs × 20% realized

Costs: Power delta + F5 license

Why tiered realization? Not all freed capacity generates immediate revenue. Emerging (20%) = growth runway | Growing (35%) = partial monetization | Established (60%) = high demand, can fill fast

🏭 GPU Cloud Economics FOR INFRASTRUCTURE PROVIDERS ▼

Neoclouds, Hyperscalers & GPU Cloud Providers

📈 Revenue Metrics (ARR per MW)

Active Power Draw

0 kW → 0 kW

Higher = GPUs doing more work

Power ↑ because utilization ↑ (good!)

Revenue Rate $/MW

$0M → $0M

How much revenue per MW

(like $/gallon - the efficiency rate)

Your Added Revenue Total

$0M

Rate × Your Power = Total

F5: 0.51MW × $19.4M/MW = $9.9M

Base: 0.36MW × $10M/MW = $3.6M

Δ = $6.3M/year

Revenue Density Lift

+0%

Rate improvement

($19.4M ÷ $10M) − 1

= 94.3% more $/MW

💰 Infrastructure Provider Margins

Gross Margin Lift

+$0.00M

additional profit per MW/year

⚡ Efficiency gains: +$0.00M

📈 More tokens served: +$0.00M

More work done = more billable revenue

Workload Density

+0%

more workloads per rack

Baseline: 1.0×

With F5: 1.0×

Higher throughput = serve more customers

Billable Utilization

+0%

more GPU-hours you can bill

Baseline: 75%

With F5: 0%

Formula: Util Lift = Throughput% × 60% (capped 95%)

Tokens/$/Watt

+0%

operational power efficiency

Baseline: 0.00

With F5: 0.00

Higher = more tokens per energy $ per watt

ARR Impact from TPDW

+$0.0M

revenue uplift per MW/year

TPDW Lift: +0%

Conversion: 50%

Efficiency gains → billable capacity

📊 Visual Comparison

Base vs F5 Performance

Technical Performance Gains

Before → With F5 (higher is better, except latency ↓)

🏗️ Infrastructure Scale Simulator INTERACTIVE

See how metrics scale from edge to hyperscale

Infrastructure Scale

1 GW

100 MW 500 MW 1 GW 5 GW 10 GW

Total GPUs

500K

Baseline ARR

$10B

F5 ARR

$14.5B

Incremental ARR

+$4.5B

At 1 GW scale: Comparable to OpenAI's infrastructure (~2 GW → $20B ARR). With F5 optimization, enable an additional $4.5B in ARR capacity annually.

📖 GPU Cloud Economics Methodology & Formulas

📈 Revenue Metrics Calculations

Cluster Power:
Baseline = GPU Count × GPU Power (W) × (0.25 + 0.75 × Utilization) ÷ 1000
With F5 = Baseline Power + (DPU Count × 50W ÷ 1000)
Example: 5,000 GPUs × 700W × 0.78 = 2,730 kW baseline

ARR per MW (Enhanced):
F5 ARR/MW = Benchmark × (1 + Throughput Lift × 0.7 + Efficiency Lift × 0.3)
70% weight on throughput (more billable tokens), 30% on efficiency (lower cost per token)

ARR Capacity Enabled:
Incremental ARR = (F5 Power MW × F5 ARR/MW) - (Baseline Power MW × Baseline ARR/MW)
Total new revenue capacity unlocked by F5 optimization

Revenue Density Lift:
Density Lift % = ((F5 ARR/MW ÷ Baseline ARR/MW) - 1) × 100
How much more revenue you generate per megawatt of power

💰 Margin & Capacity Calculations

Gross Margin Improvement per MW:
GM/MW = Power Savings/MW + Throughput Value/MW

Power Savings: $0.10/kWh × 8,760 hrs × 1,000 kW × Efficiency Gain % × 50%
Throughput Value: GPUs/MW × $2/GPU-hr × 8,760 hrs × 75% util × Throughput Gain % × 30%

Customer Density Increase:
Density Increase % ≈ Throughput Improvement %
More tokens/sec per GPU = more concurrent customers on same hardware. Tenant multiplier: 1.0× → (1 + Throughput%)×

Billable Utilization Lift:
Util Lift = Throughput Improvement % × 60%
60% of throughput gains convert to additional billable GPU-hours (capped at 95% effective utilization)

Tokens/$/Watt (Economic Power Efficiency):
Tokens/$/Watt = (Tokens/sec) ÷ (Power Cost/hr) ÷ Watts
Measures operational efficiency - tokens generated per dollar of electricity per watt consumed

⚡ Tokens/$/Watt → ARR per MW Impact at Scale

Why Tokens/$/Watt Matters for Infrastructure Providers:
Higher Tokens/$/Watt directly amplifies ARR per MW because you generate more billable output from the same power budget. At datacenter scale, even small efficiency gains compound dramatically.

Scale Impact Formula:
ARR Impact = Baseline ARR/MW × (TPDW Improvement %) × Revenue Conversion Factor
Revenue conversion ~40-60% of efficiency gains translate to bottom-line ARR improvement

📊 Example at 1 GW Scale:

• Baseline: 8.09 Tokens/$/Watt → $10B ARR capacity
• With F5: Higher Tokens/$/Watt → More tokens per energy dollar
• Result: Each % improvement in Tokens/$/Watt ≈ $100M additional ARR capacity at GW scale

🏗️ Infrastructure Scale Simulator

Scale Projections:
Baseline ARR at Scale = Scale (GW) × 1000 × ARR/MW Benchmark
F5 ARR at Scale = Baseline ARR × (1 + Revenue Density Lift %)
GPUs at Scale ≈ 1,400 GPUs per MW (at 700W per GPU)

Scale Reference Points: 100MW = Edge/Regional | 500MW-1GW = Major Neocloud | 2GW+ = Hyperscaler (OpenAI ~2GW → $20B ARR)

⚙️ Key Assumptions

• Power cost: $0.10/kWh

• GPU utilization: 75% baseline

• Blended GPU hourly rate: $2.00/hr

• DPU power: 50W per unit

• GPUs per MW: ~1,400 (at 700W)

• Throughput → Revenue: 70% weight

• Efficiency → Revenue: 30% weight

• Throughput → Util: 60% conversion

📊 Industry Benchmark Source:
Khosla Ventures AI Infrastructure Research
$10M ARR/MW benchmark for GPU cloud infrastructure. Top performers achieve $15-20M/MW.

💎 ROI Summary & KPI Cards

▼

🎨 KPI Color Coding Guide

Color Coding Thresholds

Metric

🟢 Green

🟠 Orange

🔴 Red

ROI

≥ 50%

0% – 49%

< 0%

NPV

≥ $0

—

< $0

Payback

< 18 mo

18 – 36 mo

> 36 mo / Never

IRR

> Hurdle Rate

0% – Hurdle

< 0%

🟢 Green — Strong

Proceed with confidence. Investment case is clear and defensible.

🟠 Orange — Marginal

May need optimization or strategic justification beyond pure financials.

🔴 Red — Reconsider

Does not meet criteria. Reconfigure inputs or negotiate better terms.

🔓 GPU Capacity Unlocked 🚀 Emerging (42%)

Compute freed for additional workloads

🖥️

GPUs Freed

(equivalent capacity)

⏱️

GPU-Hrs/Year

freed for other work

💵

Capacity Value

@ $3.50/GPU-hr

💡

What This Freed Capacity Could Power:

▶ Show calculation breakdown

📉 Diminishing Returns at Higher Base Utilization

F5 DPU value is highest for underutilized infrastructure. At higher base utilization, there's less inefficiency to capture, resulting in fewer equivalent GPUs freed.

🔮 KV Cache "What-If" Scenario Explorer EXPLORATORY

Explore potential additional savings from KV cache optimization — not included in main ROI above

⚠️

Note: These projections are separate from the main ROI calculations. Use this section to explore "what if" scenarios for KV cache optimization potential. Values shown here represent additional opportunity beyond the core F5 DPU benefits.

📊 Cache Type Contribution

Prefix Caching

25%

System prompts, few-shot

Semantic Caching

15%

Similar query reuse

Multi-turn Caching

20%

Conversation context

📋 Scenario Preset

👁️ View Mode

🎯 Combined KV Cache Hit Rate 60%

0% (No caching) 50% (Good) 95% (Optimal)

▶ Fine-tune individual cache types

💰 Cost Impact

$2.10

saved per hour

18%

cost reduction

$18.4K

annual savings

⚡ Performance Impact

2.5×

throughput multiplier

+750

tok/s gain

-42%

latency

🧮 Memory Efficiency

24 GB

GPU memory saved

+60%

concurrent reqs

+45%

batch size

📈 Cache Hit/Miss Visualization

60% HIT | 40% MISS

🟣 Cache Hits (reused computation) 🔴 Cache Misses (new computation)

Per 1000 Requests

600

hits

400

misses

🚀

F5 DPU Impact on KV Cache Performance

LIVE FROM CONFIG

KV Cache Overhead Offloaded

15-25%

CPU cycles freed

Effective Hit Rate Boost

+8%

from faster lookups

Utilization Boost Factor

1.00×

KV_Cache in formula

💡 What F5 DPU Does for KV Cache

Without F5 DPU

Base hit rate:

60%

Standard caching

→

F5 DPU

→

With F5 DPU

Effective hit rate:

68%

+ F5 optimization

💰 Potential Additional Benefit (Not in Main ROI)

KV cache savings $18.4K + F5 synergy $125K

+$143.4K/yr potential

📈

Economies of Scale: 1.00× multiplier applied to benefits

<128: 0.95×

256: 1.00×

512: 1.08×

1K: 1.18×

2K+: 1.30×+

What does this mean?

Larger GPU deployments achieve higher ROI due to operational efficiencies that scale non-linearly. This multiplier reflects real-world benefits including:

💰 Volume licensing — F5 discounts at scale

⚡ Operational leverage — Fixed costs spread wider

🔧 Infrastructure efficiency — Better utilization patterns

📊 Amortized overhead — Lower per-GPU admin cost

Impact: A deployment at 2,048 GPUs with a 1.30× scale factor sees 30% higher net benefits than the same configuration at 256 GPUs — directly boosting ROI, NPV, and shortening payback period.

📐

Understanding Payback Period

              Payback = (Annual F5 Cost ÷ Net Annual Benefit) × 12 months
            

Why throughput % ≠ payback speed: A 57% throughput improvement generates incremental token revenue, but payback depends on the net benefit after subtracting power costs and F5 license fees. See the financial breakdown below for your specific values.

📖 How to Read This Dashboard ▼

📊 Key Metrics Explained

Annual ROI: Your yearly return on F5 investment. Above 40% is good; above 100% is excellent.
3-Year NPV: Total value created over the license term, discounted to today's dollars. Positive = profitable investment.
Payback Period: Months until F5 costs are recovered. Formula: (Annual F5 Cost ÷ Net Annual Benefit) × 12. Under 12 months indicates strong value.
IRR: Annualized return rate. Should exceed your hurdle rate (default 12%) to justify investment.

🎯 What to Look For

Green metrics: Investment is profitable and exceeds benchmarks
Yellow metrics: Marginal returns - consider adjusting configuration
Red metrics: Negative ROI - try larger models or more complex workloads
Utilization boost: The core value driver - F5 recovers wasted GPU cycles from CPU bottlenecks

💡 Quick Interpretation: If ROI is positive, F5 DPUs generate more value than they cost. The magnitude depends on your model size (larger = better), workload complexity (agents/MoE = better), and GPU:DPU ratio (8:1 is optimal).

🤖 Analyze This

⚡ Technical Metrics & Financial Summary

▼

⚡ Technical Metrics

Your deployment: before → after → how much better

	Before	With F5	Improvement
GPU Utilization	45%	72%	+60%
Throughput (tok/s/GPU)	450	720	+60%
Tokens per Joule	0.64	0.89	+39%
Tokens/$/Watt ⓘ	0.00	0.00	+0%
TTFT Latency	150ms	120ms	-20%

💰 Financial Summary

F5 License (3-yr amortized) CapEx	$1,250,000
Incremental Token Revenue (F5)	$2,100,000
OpEx Savings	$150,000
GPU Capacity Value (20% realization)	$0
Power Cost Delta	+$85,000
Net Annual Benefit	$915,000

📋 Accounting Note: F5 license is treated as a CapEx investment (3-year upfront). Cost is capitalized and amortized over the license term on the balance sheet.

📐 How Payback Period is Calculated

                Payback (months) = (Annual F5 Cost ÷ Net Annual Benefit) × 12
              

Important: Even with a high throughput improvement (e.g., 57%), payback depends on the dollar value of that improvement after costs:

Incremental Token Revenue: Only the additional tokens/sec from F5 (not total revenue)
GPU Capacity Value: Economic value of freed GPU capacity (tiered realization rate by maturity)
OpEx Savings: Reduced orchestration, networking, and operational costs
Minus Power Delta: DPUs consume power, adding to operating costs
Minus F5 License Cost: The annual subscription fee for F5 DPUs

💡 Example: If F5 costs $100K/year and net benefit is $73.6K/year → Payback = ($100K ÷ $73.6K) × 12 = 16.3 months

📊 GPU Capacity Value: Tiered Realization Rates

Why tiered rates? Not all freed GPU capacity can be immediately monetized. The realization rate depends on your operational maturity and demand profile:

🚀 EMERGING

20%

Low demand
Capacity = growth runway

📈 GROWING

35%

Moderate demand
Some immediate use

🏢 ESTABLISHED

60%

High demand
Can fill capacity fast

Your current stage: Emerging (20%)
Calculation: 333 GPUs freed × $10.2M gross × 20% = $2.04M realized

🎯 Key Value Drivers ▼

How your configuration affects ROI

📊 Model Size Impact

Larger models = higher ROI (more memory pressure + premium pricing)

Small (7B)

~2% value

Standard (70B)

100% baseline

Frontier (405B)

~16× value

🔧 Workload Multipliers

Basic/Batch

0.65-1.0×

RAG/Agents

1.25-1.4×

MoE/Reasoning

1.45-1.5×

⚡ Your Configuration

📈 Expected ROI by Model Size (multi-agent workload, 8:1 ratio)

-90%

32B

-35%

70B

45%

175B

250%

405B

373%

671B

531%

⚠️ Basic/batch workloads show lower ROI. Break-even threshold: 70B + RAG workload (~25% ROI)

💡 How Value is Calculated:

Incremental Token Revenue = (F5 Throughput - Base Throughput) × Model $/Token × GPU Count × Hours/Year
↳ Only the additional tokens/sec from F5 are counted — not your baseline revenue

F5 Benefit = Base Boost × Model Memory Factor × Workload Factor × GPU Factor
↳ Small models: tiny KV cache (0.25×) | Large models: massive KV cache (1.5×)

F5 DPU Impact on AI Factory Economics

How infrastructure offload improves gross margins across provider types (normalized to H100, 85% baseline utilization, 400 GPUs/MW)

SYNCED WITH DASHBOARD Current Config: GPU:DPU 8:1 | F5 License $10K | KV Cache 65% | Disaggregated

85% → 96%

GPU Utilization

+$0.58M

Revenue/MW-Year

+4.2 pts

Margin Improvement

+$0.42M

Gross Profit/MW

Two different views:
• Here (AI Factory): Neocloud providers at 85% baseline → with F5: ~96%
• Dashboard tab: Your specific deployment metrics and ROI

📖 Understanding AI Factory Economics ▼

What This Tab Shows: This analysis models how F5 DPUs impact the unit economics of GPU cloud providers ("neoclouds") like IREN, CoreWeave, and Nebius. These companies rent GPU capacity and their profitability depends heavily on GPU utilization rates.

🧮 GPU Utilization Boost Calculation LIVE

              
              Loading calculation...
            

📊 Current Configuration Impact

GPU Utilization: 85% → 96%
Revenue/MW-Year: +$1.33M additional
Margin Improvement: +3.2 pts
Gross Profit/MW: +$0.90M annually

🏢 Provider Impact Summary

IREN: 35.8% → 39.0% margin
Nebius: 38.1% → 41.3% margin
CoreWeave: 30.6% → 33.8% margin

💡 Why This Matters: Even a small utilization boost translates to millions in additional annual revenue at datacenter scale.

🤖 Analyze This

📊 Scenario Economics Overview

Financial metrics for your current scenario configuration

Annual Savings

5-Year NPV

Payback Period

ROI

⚙️ Configuration Details

GPU Fleet

Model Configuration

Workload Profile

💰 Investment Required

F5 License (Annual) --

DPU Count --

GPU:DPU Ratio --

📈 Value Generated

Incremental Token Revenue --

Power Savings --

Utilization Boost --

Stream A: CPU Offload --

Stream B: AI Inference LB --

💡 Scenario Insight

📅 5-Year Financial Projection

Metric	Year 1	Year 2	Year 3	Year 4	Year 5	Total

IREN

$IREN

Bare-Metal H100 | Owned DC

Baseline Gross Margin

35.8%

→ 40.1% with F5

Revenue/MW-Year$7.80M

GPU D&A$3.50M

Power (Owned)$0.22M

DC Depreciation$0.47M

Networking$0.25M

3rd-Party Middleware$0.20M

Nebius

$NBIS

Full-Stack H100 | 60% Colo

Baseline Gross Margin

38.1%

→ 42.3% with F5

Revenue/MW-Year$9.75M

GPU D&A$3.50M

Power (Finland)$0.37M

Colo (60%)$0.72M

Ops/Platform$0.40M

Other$0.86M

CoreWeave

$CRWV

Full-Stack H100 | 80% Colo

Baseline Gross Margin

30.6%

→ 34.8% with F5

Revenue/MW-Year$8.90M

GPU D&A$3.50M

Power (Mixed)$0.42M

Colo (80%)$0.96M

Ops$0.38M

Other$0.83M

📊 F5 DPU Value Drivers for Neocloud Margins

Per MW basis, H100 normalized (400 GPUs/MW @ 85% → 90.8% utilization with F5)

Metric	IREN (Bare-Metal)	Nebius (Full-Stack)	CoreWeave (Full-Stack)

📈 Margin Improvement Waterfall (Per MW)

Breakdown of how F5 DPU contributes to gross margin enhancement

🎯 IREN Path to Full-Stack Margins

Current: IREN's bare-metal model yields 35.8% margins with owned DC advantage ($0.47M DC depreciation vs $0.72-0.96M colo costs for competitors).

With F5: Adding F5 DPU offload improves utilization to 90%+, enabling IREN to capture an additional +4.3 pts margin. Combined with their low power costs ($0.22M/MW), F5 helps IREN bridge the gap to Nebius-level margins without building full software stack.

Potential: If IREN builds full-stack on top of F5-optimized infrastructure, theoretical margin reaches 48.6%.

🚀 Nebius Margin Leadership

Current: Nebius commands highest normalized margins (38.1%) through full-stack pricing premium (+20-25% revenue/GPU-hr) and diversified customer base (Cursor, Shopify, etc.).

With F5: F5 DPU amplifies their utilization advantage (whitepaper claims 100% benchmark performance). Moving from 95-97% to near-100% effective utilization captures additional +4.2 pts margin.

Moat: Customer base diversification + F5-enhanced platform performance creates sustainable competitive advantage vs colo-heavy competitors.

📋 Methodology & Assumptions

This analysis normalizes neocloud GPU pricing economics from public filings and industry research:

GPU normalization: H100, 4-year depreciation ($3.50M/MW)
Utilization baseline: 85% (industry standard for datacenter-scale infrastructure)
Revenue/GPU-hr: $2.50-2.75 bare-metal, $2.80-3.50 full-stack (historical pricing)
Infrastructure: 400 GPUs per MW with full networking, cooling, InfiniBand-class interconnect
F5 DPU impact: +5.8% utilization boost (from CPU offload), networking efficiency gains, reduced middleware overhead
Debt not included: CoreWeave's $1.3B/year interest would further pressure margins

💰 Revenue/MW-Year Calculation

Base Formula:

            Revenue/MW-Year = GPUs/MW × $/GPU-hr × Hours/Year × Utilization
          

H100 Baseline Example (IREN Bare-Metal):

GPUs per MW	400 (H100 @ 700W each)
$/GPU-hr (bare-metal)	$2.65 (market rate)
Hours/Year	8,760
Utilization	85%
= Revenue/MW-Year	$7.89M ≈ $7.80M

GPU Performance Scaling:

Revenue scales with GPU throughput multiplier (faster GPUs command higher prices):

GPU	Multiplier	GPUs/MW	IREN Revenue
H100	1.0×	400	$7.80M
B100	1.4×	280	$10.92M
B200	1.69×	280	$13.18M
GB200	2.5×	280	$19.50M

F5 DPU Revenue Uplift:

            New Revenue = Base Revenue × (Baseline Util + F5 Boost) / Baseline Util

            Example: $7.80M × (85% + 5.8%) / 85% = $8.33M (+$0.53M uplift)

Note: Full-stack providers (Nebius, CoreWeave) command 15-25% revenue premiums over bare-metal due to managed services, ML frameworks, and customer support bundled into pricing.

🧠 Model Size Impact on F5 Value

Why Model Size Matters:

F5 DPU value scales with model size because larger models have bigger KV caches, higher memory pressure, and more CPU overhead to offload. Small models are compute-bound with minimal memory bottlenecks.

Model Size	F5 Benefit	$/1M Tokens	Rev Factor	Expected ROI
1B - 8B	0.15× - 0.30×	$0.03 - $0.10	0.03× - 0.10×	Negative (-90%)
13B - 32B	0.45× - 0.70×	$0.15 - $0.40	0.15× - 0.40×	Negative to marginal
70B - 72B	1.0×	~$1.00	1.0× (base)	40-60% (baseline)
175B	1.20×	~$5.00	5.0×	200-300%
405B	1.35×	~$12.00	12.0×	350-400%
671B	1.50×	~$18.00	18.0×	500%+

Key Insight: F5 DPU ROI scales dramatically with model size. Small models (7B-13B) show negative ROI because they lack significant memory pressure and command commodity pricing. The break-even point is around 32B-70B models. Frontier models (405B+) show 300%+ ROI due to premium pricing ($12-15/1M tokens vs $1/1M for 70B) combined with severe memory bottlenecks that F5 KV cache offload directly addresses.

Formulas applied:
• Utilization Boost = Base Boost × Model F5 Factor × [other factors]
• Revenue Value = Throughput Gain × Base $/Token × Model Revenue Factor

🧪 Tolly Report #226104 — AI Inference Load Balancing (March 2026)

Source: Tolly Enterprises, LLC. Test Report #226104. F5 BIG-IP Next for Kubernetes (BNK) on DPU v2.2.0.

Two Value Streams:

Stream A — CPU Offload: ~83% CPU reduction (2 vs 12 cores). Frees ~10 cores/server.
Value = Servers × 10 cores × $/core-hr × 8,760 hrs × Workload multiplier + CPU power savings

Stream B — AI Inference LB: GPU-aware traffic steering (vs HAProxy):
1B: +406% throughput, 96% TTFT, 80% latency | 8B: +114%, 76%, 53% | 70B: +40%, 61%, 29%
Value = Additional tokens/sec × GPUs × 3600 × 8,760 × $/token + TTFT retention

Report: tolly.com/publications/226104

📖 Understanding Sensitivity Analysis ▼

What This Tab Shows: Sensitivity analysis reveals which input parameters have the greatest impact on your ROI, helping you understand where uncertainty matters most and where to focus negotiation or optimization efforts.

🌪️ Tornado Chart

Longer bars = higher sensitivity: Parameters with wide bars have outsized impact on ROI
Red (left): ROI when parameter decreases by 50%
Green (right): ROI when parameter increases by 50%
Focus on top bars: These are your key risk/opportunity levers

🗺️ Heatmap & Elasticity

Heatmap: Shows ROI for all combinations of F5 cost vs GPU:DPU ratio
Green zones: Profitable configurations to target
Red zones: Configurations to avoid
Elasticity >1: ROI changes faster than the parameter (high risk/reward)

💡 How to Use This: If F5 license cost has the longest bar, negotiate hard on pricing. If model size dominates, prioritize larger model deployments.

🤖 Analyze This

🌪️ Tornado Charts - Multi-Metric Sensitivity Analysis

Which input variables have the biggest impact? (10+ parameters analyzed)

Auto-update

🌪️

ROI Sensitivity

Discover which inputs drive your ROI the most

Click "Run Analysis" to generate tornado charts

🗺️ Parameter Interaction Heatmap

Explore how two parameters interact to affect outcomes

Y-Axis:

X-Axis:

Ranges centered around your current configuration values

Analysis will appear after running sensitivity

📋 Elasticity Analysis

Parameter	Low Value	Base Value	High Value	ROI @ Low	ROI @ High	Elasticity	Risk Level
Run analysis to populate

📖 Understanding Monte Carlo Simulation ▼

What This Tab Shows: Monte Carlo simulation runs thousands of scenarios with randomized inputs to show the full range of possible ROI outcomes. Instead of a single point estimate, you see the probability distribution of returns.

📊 Histogram & Statistics

Mean ROI: Average outcome across all simulations
P10 (Downside): 90% of outcomes are better than this - your "worst realistic case"
P50 (Median): Half of outcomes are above, half below - the "typical" result
P90 (Upside): Only 10% of outcomes are better - your "best realistic case"
P(ROI > 50%): Probability of achieving above-target returns

📈 Value at Risk (VaR)

VaR 95%: Maximum expected loss in 95% of scenarios
CVaR (Expected Shortfall): Average loss in the worst 5% of scenarios
Sharpe Ratio: Risk-adjusted return (higher = better risk/reward)
Narrow histogram: Low uncertainty, predictable outcomes
Wide histogram: High uncertainty, variable outcomes

💡 How to Use This: If P10 (downside) is still positive, the investment is robust. Present P10-P50-P90 range to stakeholders as realistic bounds.

🤖 Analyze This

🎲 Monte Carlo Simulation

10,000 iterations with triangular distributions

Click to run Monte Carlo simulation

📈 Value at Risk (VaR) Analysis

VaR 95%

5% chance of worse outcome

Expected Shortfall (CVaR)

Average of worst 5%

Probability of Loss

ROI < 0%

📖 Understanding Scenario Comparison ▼

What This Tab Shows: Scenario comparison lets you evaluate different deployment strategies side-by-side, from conservative to aggressive configurations. Use this to find the optimal balance of risk and return for your organization.

🎯 Predefined Scenarios

Conservative: Lower risk, modest returns (sparse ratio, standard workloads)
Balanced: Optimal risk/reward tradeoff (8:1 ratio, mixed workloads)
Aggressive: Maximum ROI, higher execution risk (dense deployment, frontier models)
Your Config: Current settings for direct comparison

📊 Metrics to Compare

ROI: Annual return on investment (higher = better)
NPV: Total value created over license term
Payback: Time to recover investment (shorter = lower risk)
Rating: Overall assessment (⭐ to ⭐⭐⭐⭐⭐)

💡 How to Use This: Start with "Balanced" scenario as your baseline. Use the Custom Scenario Builder to test specific "what-if" configurations.

🤖 Analyze This

🎯 Predefined Scenario Comparison

Compare your configuration against optimized scenarios

Scenario	GPU:DPU	F5 Cost	ROI	NPV	Payback	Rating
Click "Run Comparison" to analyze scenarios

🔧 Custom Scenario Builder

Scenario A (Current)

GPU:DPU Ratio	1:8
F5 Cost	$10,000
ROI	101%
NPV	$2.1M

Scenario B (Optimized)

GPU:DPU Ratio	1:16
F5 Cost	$8,000
ROI	185%
NPV	$3.8M

📖 Understanding TCO Comparison ▼

What This Tab Shows: Total Cost of Ownership (TCO) compares the full financial picture over 1-5 years: all costs (hardware, power, operations, licensing) versus all value generated (throughput, efficiency gains).

🔴 Without F5 (Baseline)

GPU CapEx: Hardware investment for your GPU fleet
Power & Cooling: Electricity costs at your utilization rate
Operations: Staff, maintenance, software overhead
Throughput Value: Revenue potential at baseline utilization

🟢 With F5 DPU

Added DPU Cost: F5 hardware + licensing investment
Power Change: Slight increase from DPUs, offset by efficiency
OpEx Reduction: Lower orchestration and management overhead
Enhanced Throughput: Higher revenue from improved utilization

💡 Key Insight: The "Net TCO Advantage" shows total savings. The "Effective $/GPU-Hour" metric is crucial for comparing against cloud alternatives.

🤖 Analyze This

🔮

What-If: Include KV Cache Optimization EXPLORATORY

Add $0/yr to F5 benefits

Metric	Without KV Cache	With KV Cache	Improvement
Loading...

📈 Total Cost of Ownership Comparison

Side-by-side comparison: Without F5 vs With F5 DPU

WITHOUT F5 (Baseline)

🔴

Hardware CapEx

GPU Infrastructure $80,000,000

DPU Hardware $0

Power & Cooling

Annual Power Cost $5,200,000

Total Power (3 yr) $15,600,000

Operations

Annual OpEx $1,000,000

Total OpEx (3 yr) $3,000,000

Licensing

F5 DPU License $0

TOTAL TCO $98,600,000

Incremental Token Revenue $0

Effective TCO (after value) $98,600,000

WITH F5 DPU

🟢

Hardware CapEx

GPU Infrastructure $80,000,000

DPU Hardware $1,250,000

Power & Cooling

Annual Power Cost $5,800,000

Total Power (3 yr) $17,400,000

Operations

Annual OpEx $850,000

Total OpEx (3 yr) $2,550,000

Licensing CapEx

F5 DPU License (Upfront) $1,250,000

Total License (3 yr) $3,750,000

TOTAL TCO $104,950,000

Incremental Token Revenue +$12,600,000

├ Stream A: CPU Offload $0

├ Stream B: AI Inference LB $0

Effective TCO (after value) $92,350,000

DIFFERENCE

📊

Hardware CapEx

GPU Infrastructure $0

DPU Hardware +$1,250,000

Power & Cooling

Annual Power Cost +$600,000

Total Power Delta +$1,800,000

Operations

Annual OpEx -$150,000

Total OpEx Savings -$450,000

Licensing CapEx

F5 License Cost +$3,750,000

TCO DELTA +$6,350,000

Incremental Token Revenue +$12,600,000

NET SAVINGS $6,250,000

Effective TCO Reduction

6.3%

vs baseline infrastructure

Cost per GPU Hour

$1.28 → $1.19

-7.0% reduction

Utilization-Adjusted Value

+$4.2M

Additional capacity unlocked

Break-Even Timeline

8.2 months

Time to positive ROI

💎

4-Model Pricing Comparison

CapEx vs Subscription vs Per-Token vs Incremental Token

MODEL 1

CapEx (Upfront)

Annual Cost to Customer

F5 Annual Revenue

ROI

Payback

-- mo

MODEL 2

Subscription

Annual Cost to Customer

F5 Annual Revenue

ROI

Payback

-- mo

MODEL 3

Per-Token

F5 Revenue (per-token)

Customer Revenue

F5 as % of Customer

Value Gap (Customer Keeps)

MODEL 4

Incremental Token

F5 Revenue (incr. only)

Incremental Tokens/sec

Customer Incr. Revenue

Value Gap (Customer Keeps)

Total Tokens/sec (with F5)

Tokens/Year (with F5)

Incremental Tokens/Year

How to read this: Models 1-2 are traditional license models. Model 3 (Per-Token) charges F5 a fraction of every token processed. Model 4 (Incremental) charges only for the additional tokens F5 DPUs enable beyond baseline. The "Value Gap" shows how much revenue the customer retains after F5's fee.

📊

CapEx vs Subscription Crossover Analysis

See when each payment model becomes more economical

Crossover Point

-- mo

When CapEx becomes cheaper

3-Year CapEx Total

Upfront + amortized

3-Year Subscription

Annual payments × 3

Recommendation

How to read this chart: The blue line shows cumulative CapEx cost (high upfront, then flat). The purple line shows cumulative Subscription cost (starts at zero, increases monthly). Where they cross is the breakeven point — if your deployment horizon is longer than this, CapEx is more economical.

📖 Understanding Cash Flow Projections ▼

What This Tab Shows: Year-by-year breakdown of all cash inflows and outflows from F5 DPU deployment, including discounted cash flows (DCF) for proper time-value-of-money analysis.

📊 Column Definitions

Investment: F5 licensing cost (Year 0 = initial, subsequent = renewals)
Throughput Value: Revenue from improved GPU utilization
OpEx Savings: Reduced operational costs (management, orchestration)
Power Delta: Net change in electricity costs
Net Cash Flow: Total benefit minus total costs for each year

📈 Key Metrics

Discounted CF: Today's value of future cash flows (at your discount rate)
Cumulative: Running total - when this turns positive, you've broken even
Year 0: Initial investment (negative cash flow)
Years 1+: Should show positive net cash flow if ROI > 0

💡 How to Use This: Export the CSV for financial modeling and board presentations. The cumulative chart shows your payback trajectory visually.

🤖 Analyze This

🔮

What-If: Include KV Cache Optimization EXPLORATORY

Add $0/yr to projections

💵 Year-by-Year Cash Flow Projection

Detailed financial timeline with cumulative analysis

How to read this table: Token Revenue = all token income (utilization gain + Stream B: AI Inference LB throughput) CPU Offload = cost savings from freeing host CPU cores via DPU (not token revenue)

✓ Columns should add up: Token Revenue + CPU Offload + OpEx Savings − Power Delta − F5 License ≈ Net CF | Hover column headers and cells for detailed breakdowns

Year	DPU Hardware	Incr. Revenue	OpEx Savings	Power Delta	F5 License	Net Cash Flow	Discounted CF	Cumulative

Token Revenue (incl. Stream B: AI Inference LB) REVENUE

Token income from F5 DPU, combining: utilization gain (higher GPU efficiency) and Stream B: AI Inference LB (GPU-aware load balancing → more tokens/sec, Tolly 21–406% throughput boost). Both are fundamentally the same: more tokens served = more revenue.

Annual Token Revenue

Term Total

CPU Offload (Stream A) COST SAVINGS

DPU-based load balancing frees host CPU cores, reducing compute costs and power draw. This is not token revenue — it's infrastructure cost avoidance. Validated by Tolly #226104 (F5 BNK uses 2 cores vs HAProxy's 12).

Annual Cost Savings

Term Total

📈 Cumulative Cash Flow Chart

📖 Understanding Break-Even Analysis ▼

What This Tab Shows: Break-even analysis identifies the critical thresholds for each parameter - the maximum cost you can pay or minimum scale you need for F5 DPUs to remain profitable.

📍 Break-Even Cards

Max F5 Cost: Highest $/DPU license that keeps ROI ≥ 0%
Min GPU Count: Smallest deployment that remains profitable
Max Electricity Rate: Highest power cost before margins go negative
Bar indicator: Green = safe margin, Yellow = close to threshold, Red = over limit

🎯 ROI Threshold Table

Target ROI column: Desired return hurdles (25%, 50%, 100%)
Required values: What each parameter must be to hit that target
Use for negotiation: "We need F5 cost ≤ $X to hit our 50% ROI hurdle"
Feasibility check: If required values are unrealistic, adjust expectations

💡 How to Use This: Before negotiations, know your break-even F5 cost - this is your walk-away price.

🤖 Analyze This

🔮

What-If: Include KV Cache Optimization EXPLORATORY

Add $0/yr to benefit

📊 Value Stream Contribution to Break-Even

Stream A: CPU Offload

0% of gross benefit

Stream B: AI Inference LB

0% of gross benefit

Combined Streams

0% of gross benefit

📍 Break-Even Analysis

Parameter values where ROI = 0% (holding others constant)

Max F5 Cost

$--

Current: $10,000

Min GPU Count

Current: 1,000

Max Electricity Rate

$--/kWh

Current: $0.12/kWh

🎯 ROI Threshold Analysis

Parameter values to achieve specific ROI targets

Target ROI	Required F5 Cost ≤	Or DPU:GPU Ratio ≥	Or GPU Count ≥	Achievable?
Click "Calculate Break-Even" to analyze

💾 F5 + Context-Aware Storage Integration

NVIDIA ICMS Platform - CES 2026

NVIDIA BlueField-4 powers the Inference Context Memory Storage Platform, a new class of AI-native storage infrastructure for gigascale inference. Configure storage synergy to include infrastructure costs in TCO calculations and unlock additional F5 DPU performance benefits.

Higher Tokens/Sec

Better Power Efficiency

20-40%

TTFT Reduction

⚙️ Storage Configuration

Enable Storage Synergy

⚡ Quick Presets

🏢 Storage Vendor

🔗 Interconnect

💰 Storage Infrastructure Cost (CapEx)

Required Capacity

0 TB

(16 TB × GPU count)

Cost per TB

Total Storage CapEx

Added to TCO calculations

🤝 NVIDIA ICMS Partners (CES 2026)

✓ Pure Storage (FlashBlade)

✓ WEKA (NeuralMesh)

✓ DDN (AI7990)

✓ VAST Data

✓ NetApp (ONTAP AI)

✓ Dell (PowerScale)

✓ HPE (Alletra, Cray)

✓ IBM (Storage Scale)

✓ Hitachi (VSP 5600)

✓ Nutanix

✓ Supermicro

✓ Cloudian

⚠️ Pricing Note: Storage pricing varies significantly by configuration and volume. Values marked "speculative" are rough estimates. Contact vendors for actual pricing quotes.

📋 ROI Calculation Methodology

🤖 Analyze This

Model Overview

This calculator models F5 DPU ROI using a multi-factor approach that accounts for model size, workload complexity, GPU:DPU ratio, and infrastructure configuration. The model has been calibrated against industry benchmarks and real-world neocloud economics (IREN, CoreWeave, Nebius).

📐 Core ROI Formula

Annual ROI = (Net Benefit / F5 Investment) × 100%
Where:
Net Benefit = Token Revenue + OpEx Savings + CPU Offload (A) + AI Inference LB (B) + GPU Capacity Value - Power Delta - F5 Cost

                GPU Capacity Value = GPUs_Freed × GPU_Hours/Year × GPU_Hourly_Rate × Realization_Rate
              

                Realization Rate varies by maturity: Emerging 20% | Growing 35% | Established 60%
              
Payback Period = (Annual F5 Cost / Net Benefit) × 12 months

🧪 Tolly Report #226104 — Separated Value Streams (March 2026)

Stream A: CPU Offload (DPU-validated)

F5 BNK on DPU uses ~2 CPU cores vs HAProxy's ~12 cores — an 83% reduction. This frees ~10 cores per server for AI application processing.

              Value = Servers × 10 cores × $Core-hour × 8,760 hrs × Workload Multiplier + CPU Power Savings
            

Stream B: AI Inference Load Balancing (Tolly-validated)

GPU-aware traffic steering avoids sending requests to busy GPUs. Tolly tested three models vs HAProxy:

Metric	1B	8B	70B
Throughput	+406%	+114%	+40%
TTFT	96%	76%	61%
Latency	80%	53%	29%

              Value = Additional Tokens/sec × GPUs × 3600 × 8,760 × $/Token + TTFT Retention Value
            

Source: Tolly #226104 — F5 BNK on DPU v2.2.0, NVIDIA GH200, AIPerf, Feb 2026.

🔧 CPU Offload Breakdown

The "CPU Tax" in AI Inference

In traditional AI inference architectures, CPUs handle significant overhead tasks that prevent GPUs from reaching full utilization. F5 DPUs offload these functions to dedicated hardware, freeing CPU cycles and enabling higher GPU throughput.

CPU Function	Overhead %	F5 Offload	Technical Details
KV Cache Management	15-25%	✓ Full	Grows with context length; memory allocation, cache eviction policies, attention state management
Network I/O & Protocols	10-15%	✓ Full	TCP/IP stack, gRPC/REST parsing, response assembly, connection pooling
SSL/TLS Processing	8-12%	✓ Full	Encryption/decryption, certificate validation, TLS handshakes
Memory & DMA Operations	8-12%	◐ Partial	Buffer management, zero-copy transfers, RDMA coordination
Request Batching & Scheduling	5-10%	◐ Partial	Dynamic batching decisions, queue management, priority scheduling
Load Balancing & Security	5-10%	✓ Full	Request routing, health checks, WAF, rate limiting, DDoS mitigation
Tokenization & Telemetry	3-8%	○ None	Pre/post processing, metrics collection, distributed tracing
TOTAL CPU OVERHEAD	54-92%	~58% Recovery	Weighted by model size and workload complexity

📊 Why Larger Models Benefit More

Larger models have proportionally more CPU-bound operations, especially KV cache management which grows with model parameters and context length.

~27% overhead

→ +8-12% util

32B

~34% overhead

→ +12-18% util

70B

~38% overhead

→ +18-25% util

175B

~44% overhead

→ +25-32% util

405B+

~50% overhead

→ +30-40% util

Formula: Total_Overhead = Base_Overhead × Model_Scale_Factor where scale factors range from 0.7× (7B) to 1.3× (405B)

🏭 GPU Cloud Economics Methodology

Understanding GPU Cloud Economics for Infrastructure Providers

The GPU Cloud Economics section is designed specifically for Neoclouds, Hyperscalers, and GPU Cloud Providers who measure success in terms of revenue per megawatt, customer density, and infrastructure efficiency. Unlike traditional enterprise ROI (which focuses on cost savings), cloud economics focuses on revenue maximization from constrained resources—power, space, and capital.

🎯 Why This Matters

In GPU cloud infrastructure, power is the fundamental constraint. A data center with 100MW of power capacity can't add more GPUs without building new infrastructure. F5 DPUs help providers extract more revenue from the same power envelope by:

Increasing GPU utilization → More billable compute hours from the same hardware
Improving throughput → Serve more customers/requests with the same GPU count
Reducing CPU bottlenecks → GPUs spend more time on inference, less time waiting
Enabling higher customer density → More tenants per rack without performance degradation

📊 Key Metrics Explained

💰 ARR per MW (Revenue Rate)

What it measures: Annual Recurring Revenue generated per megawatt of power consumed. This is the fundamental efficiency metric for GPU cloud providers.

                  ARR/MW = Total Annual Revenue ÷ Power Consumption (MW)
                

Industry benchmark: $10M/MW (average), $15-20M/MW (top performers like OpenAI)

📈 Incremental ARR (Your Added Revenue)

What it measures: The total additional revenue your specific cluster can generate with F5 optimization—calculated as the difference between F5-enhanced and baseline revenue.

                  Incremental ARR = (F5 Power × F5 Rate) - (Base Power × Base Rate)
                

Key insight: This is your actual dollar uplift, not a percentage improvement

⚡ Revenue Density Lift

What it measures: The percentage improvement in revenue efficiency—how much more revenue you generate per unit of power with F5 vs. without.

                  Density Lift % = ((F5 ARR/MW ÷ Baseline ARR/MW) - 1) × 100
                

Typical range: 40-80% density lift depending on model size and workload

👥 Customer Density Increase

What it measures: How many more concurrent customers/tenants you can serve on the same infrastructure due to improved throughput.

                  Density Increase ≈ Throughput Improvement %
                

Business impact: More customers without new capital expenditure

🔗 The F5 Value Chain: How DPUs Drive Revenue

🔧

CPU Offload

KV cache, network I/O, SSL/TLS moved to DPU

→

⚡

GPU Liberation

GPUs focus 100% on inference compute

→

📊

Higher Utilization

36% → 63%+ effective utilization

→

💰

Revenue Density

40-80% more $/MW

Calculation Methodology:

1. Throughput Contribution (70% weight): More tokens/second = more billable API calls. F5 enables 50-100%+ throughput improvement depending on model size.

2. Efficiency Contribution (30% weight): Lower cost per token enables competitive pricing while maintaining margins.

                  F5 ARR/MW = Baseline ARR/MW × (1 + Throughput_Lift × 0.7 + Efficiency_Lift × 0.3)
                

🏗️ Scale Considerations: From Edge to Hyperscale

100 MW

Edge/Regional

~140K GPUs

500 MW

Major Neocloud

~700K GPUs

2 GW

Hyperscaler

~2.8M GPUs

10 GW

Global AI Infra

~14M GPUs

Industry Reference: OpenAI operates approximately 2GW of GPU infrastructure generating an estimated $20B+ ARR, validating the ~$10M/MW benchmark. At this scale, a 50% revenue density improvement from F5 represents $10B+ in additional annual revenue capacity.

📖 Interpreting Your Results

Metric	Good	Excellent	What It Means
ARR/MW (F5)	$12-15M	$15M+	Revenue efficiency per megawatt exceeds industry average
Revenue Density Lift	40-60%	60%+	Significant uplift in revenue per unit of power consumed
Billable Utilization	55-70%	70%+	GPUs are generating revenue more hours per day
Customer Density	+30-50%	+50%+	More tenants served without additional hardware

⚙️ Configuration Manager ADMIN

▼

Add new GPU types, model architectures, or update existing configurations. Changes persist in browser storage and can be exported/imported.

Config Version: 2.0.0

Last Updated: Never

Source: Local

Current GPU Types

Add/Edit GPU

GPU Name

Memory (GB)

Bandwidth (TB/s)

TDP (Watts)

Generation

Hourly Cost ($)

Release Year

Speculative

🖥️ GPU Requirements Matrix

📊 Interactive GPU Calculator INTERACTIVE

Calculate exact GPU requirements based on model size, precision, and context window. Memory formula: Params × Bytes_per_Param + KV_Cache_Overhead

Precision

Context Window

Batch Size

GPU Type

Model	Params	Model Mem	KV Cache	Total	GPUs Needed	Status

Single GPU

Multi-GPU (Tensor Parallel)

Multi-Node Required

Speculative (1T+)

Memory Calculation:
Model_Memory = Parameters × Bytes_per_Precision × 1.1 (activations overhead)
KV_Cache = 2 × Layers × Hidden_Dim × Context × Batch × Bytes × 1.2 (fragmentation)
GPUs = ceil(Total_Memory / GPU_Memory × 0.85 utilization factor)

📋 Quick Reference: Minimum GPUs per Model (FP16, 32K context)

7B-8B

1 GPU

~20GB needed

13B

1 GPU

~35GB needed

32B

1-2 GPU

~85GB needed

70B

2-4 GPU

~180GB needed

175B

4-8 GPU

~450GB needed

405B

8-16 GPU

~1TB needed

671B

16-32 GPU

~1.6TB (MoE)

1T+

32+ GPU

Speculative

Note: Add 2-4x more GPUs for production throughput (replication). MoE models (671B+) only activate ~10-20% of params per token.

🏢 Deployment Scale Matrix

📊 GPUs Needed by Deployment Tier & Concurrent Users REFERENCE

Estimate total GPUs needed based on deployment tier and target concurrent users. Assumes 70B model, H100 GPUs, ~50 tok/s per replica, ~500 tokens per request.

Deployment Tier	Concurrent Users	Replicas Needed	8B GPUs	70B GPUs	405B GPUs	Typical Use Case
🔬 POC/Pilot	5-10	1-2	1-2	4-8	16-32	Internal testing, demos
📈 Small Prod	20-50	4-8	4-8	16-32	64-128	Single product, limited users
🏢 Department	100-200	16-24	16-24	64-96	256-384	Team-wide AI assistant
🎯 Startup	500-1K	40-80	40-80	160-320	640-1.2K	Growing SaaS product
🏛️ Enterprise	5K-10K	200-400	200-400	800-1.6K	3.2K-6.4K	Fortune 500, multi-product
☁️ Cloud Provider	50K-100K	2K-4K	2K-4K	8K-16K	32K-64K	AI API service (OpenAI-scale)
🔬 Frontier Lab	500K+	10K+	10K+	40K+	160K+	Research + massive inference

Calculation Assumptions:
• Replicas = Concurrent_Users × Avg_Request_Duration / Target_Latency (assumes ~10s request, <2s latency target)
• GPUs per replica: 8B=1, 70B=4, 405B=16 (H100, FP16, 32K context)
• Throughput: ~50 tok/s per replica at 70B, scales inversely with model size
• Add 20-30% for redundancy/failover in production

🧮 Custom Sizing Calculator

Target Concurrent Users

Model Size

GPU Type

💡 Why do GPUs increase with more users?

Each user needs GPU compute time. LLM inference requires loading model weights into GPU memory and performing matrix multiplications for every token generated. Here's the math:

Throughput per replica: A 70B model on 4× H100s can handle ~12 requests/min
Replicas needed: 100 concurrent users ÷ 12 req/min = ~9 replicas
GPUs needed: 9 replicas × 4 GPUs/replica = 36 GPUs base
+25% HA: Add redundancy for failover = 45 GPUs total

Larger models (405B, 1T+) need more GPUs per replica, so scaling is steeper. Newer GPUs (H200, B200) have more memory, requiring fewer GPUs per replica.

⚡ F5 Utilization Boost Calculation

🏭 AI Factory Economics Formula LIVE

Util_Boost = Base × KV_Cache × Workload × Disagg × Ratio × GPU_Gen × Model
Where Base = 5.0%, normalized to 85% baseline utilization (minimum floor: 2.5%)

Loading live calculation...

📊 Dashboard Technical Metrics Formula LIVE

F5_Util = Base_Util + (CPU_Overhead × Recovery × Workload × Model × Ratio × Disagg)
Shows your deployment-specific baseline → with F5 improvement

Loading live calculation...

📋 Parameter Reference

Parameter	Your Value	Description
`Base`	5.0%	Base utilization boost at default settings (min floor: 2.5%)
`KV_Cache`	1.00	KV Cache efficiency / 65 (your: 65%)
`Workload`	0.85	Workload complexity (basic=0.85, realtime=0.95, moe=1.2)
`Disagg`	1.08	Disaggregated serving bonus (1.08× if enabled)
`Ratio`	1.00	GPU:DPU ratio (4:1=1.16, 8:1=1.0, 16:1=0.68, 32:1=0.04)
`GPU_Gen`	1.00	GPU generation (V100=0.48, H100=1.0, GB300=2.52)
`Model`	1.00	Model size factor (7B=0.25, 70B=1.0, 405B=1.35)
Result	+5.0%	Utilization boost (85% → 89%)

💾 NVIDIA Inference Context Memory Storage Platform (ICMS)

NVIDIA CES 2026 Announcement: NVIDIA BlueField-4 powers the Inference Context Memory Storage Platform (ICMS) — a new class of AI-native storage infrastructure enabling gigascale inference with 5× tokens-per-second, 5× power efficiency, and 20-40% reduction in time-to-first-token (TTFT).

🔷 NVIDIA BlueField-4 DPU Specifications

CPU

64-core NVIDIA Grace

Networking

ConnectX-9 (800 Gbps)

Availability

H2 2026

Bandwidth vs BF3

2× higher

Memory BW vs BF3

3× higher

Compute vs BF3

6× higher

Storage/DPU

150 TB

Per Appliance

600 TB (4 DPUs)

Per Rack

9.6 PB

Context Mem/GPU

16 TB

📊 ICMS Verified Performance Metrics

5×

Tokens/Second

5×

Power Efficiency

20-40%

TTFT Reduction

99%

GPU Utilization

G3.5

Memory Tier

G3.5 Memory Tier sits between GPU HBM (G1) and general storage (G4), enabling intelligent context caching.

Storage Synergy Formula:
F5_Total_Impact = F5_Base_Boost × Storage_Synergy_Multiplier
Where:

              Storage_Synergy = Base(1.15) + Vendor_Bonus + ICMS_Bonus + (Interconnect_Bonus × KV_Hit_Rate)

              Capped at maximum 1.35× multiplier
            

🤝 Confirmed NVIDIA ICMS Partners (CES 2026)

DDN Infinia WEKA NeuralMesh VAST Data Pure Storage IBM Storage Scale Dell PowerScale HPE Cray NetApp Hitachi Nutanix Supermicro Cloudian AIC

Component	Impact Range	Description
`KV-Cache Offload`	+25%	Intelligent offloading of KV-cache to BlueField-4 NVMe storage (G3.5 tier)
`CXL Memory Extension`	+20%	CXL 3.0/4.0 memory pooling extends effective GPU memory up to 16TB/GPU
`GPUDirect RDMA`	+8%	Direct GPU-to-storage via ConnectX-9 (800 Gbps) bypasses CPU overhead
`ICMS Partner Bonus`	+2% to +5%	BlueField-4 certified vendors with optimized DOCA integration
`Vendor Synergy`	+3% to +22%	Storage vendor-specific F5 integration (WEKA NeuralMesh, DDN Infinia, etc.)
`TTFT Reduction`	12% to 30%	Time-to-first-token improvement varies by vendor optimization level
`Interconnect Bonus`	+0.5% to +5%	NVLink 6, CXL 4.0, InfiniBand XDR/GDR provide additional gains

🔧 Key ICMS Technology Components

NVIDIA Dynamo: Open-source disaggregated inference serving (WEKA, IBM, Dell integration)
NIXL: NVIDIA Inference Xfer Library for optimized storage-to-GPU data movement
DOCA Framework: BlueField-4 software stack for storage offload acceleration
NeuralMesh (WEKA): Augmented Memory Grid providing transparent context caching
NFS-over-RDMA: High-performance NFS with RDMA transport (Dell PowerScale)
AI OS Native (VAST): Storage OS running directly on BlueField-4 DPUs

Enable in Admin Configuration → Storage tab. Select a storage vendor and interconnect technology to model the combined F5 + Context-Aware Storage impact on ROI calculations. Speculative Vera Rubin-era storage systems are marked with estimated 2027+ availability.

💰 Incremental Token Revenue Calculation

Key Concept: This is the additional revenue from F5's throughput improvement — not total token revenue. It only counts the extra tokens/sec that F5 enables beyond baseline.

Incremental_Revenue = (F5_Throughput - Base_Throughput) × GPU_Count × 3600 × Hours/Year × $/Token

Key insight: Token pricing varies dramatically by model size. Small models (7B) charge ~$0.07/1M tokens while frontier models (405B) charge ~$12/1M tokens. This 170× pricing difference is the primary driver of ROI variation.

🏋️ Training Value Calculation

⚠️ Important Note: Training value calculations use estimated efficiency gains. The default 22% is a conservative estimate based on general DPU benefits for data loading, gradient sync, and I/O optimization. Users should adjust this value based on measured workload characteristics.

When "Training" or "Mixed" use case is selected, the calculator computes value from three components:

Component	Formula	Basis
⚡ Training Efficiency Gains	GPU_Hours × GPU_Rate × (Efficiency% × Model_Factor)	Faster data loading (12-18%) Gradient sync improvement (5-10%) I/O optimization (5-8%) Default: 22% (configurable 5-40%)
📊 Data Pipeline Acceleration	GPU_Count × $500/year × Model_Factor	Network/storage offload benefits Reduced data loading bottlenecks Estimate: ~$500/GPU/year
💾 Checkpoint Optimization	GPU_Count × $300/year × Model_Factor	Faster checkpoint save/restore Reduced training interruption time Estimate: ~$300/GPU/year

Total Training Value = Efficiency_Gains + Data_Pipeline + Checkpoint_Optimization

⚠️ Transparency Notice:

The $500/GPU/year (data pipeline) and $300/GPU/year (checkpointing) are estimates based on general DPU benefits
These values are NOT sourced from specific F5 benchmarks
Actual benefits will vary significantly based on workload characteristics, storage architecture, and network topology
The Training Efficiency % is configurable (5-40%) — adjust based on your measured results

Mixed Mode (50/50): When "Mixed" use case is selected, the calculator blends inference value and training value equally:

Mixed_Value = (Inference_Value × 0.5) + (Training_Value × 0.5)

🔀 Disaggregated Serving Bonus

What Is Disaggregated Serving? Disaggregated (or "splitwise") serving separates LLM inference into two distinct phases running on different GPU pools: Prefill (prompt processing) and Decode (token generation). This architecture is increasingly adopted by high-scale AI deployments.

Boost Location	Multiplier	Applied To
Utilization Calculation	1.12×	F5 utilization boost (f5Boost)
Neocloud Impact	1.08×	Utilization projections for neocloud economics
CPU Overhead	+5%	Additional coordination overhead (which F5 then recovers)

🎯 Why Disaggregated Benefits More from F5 DPU:

🌐 Network Coordination

Prefill→Decode handoffs create network traffic that F5 optimizes through intelligent traffic shaping

💾 KV Cache Transfer

Transferring KV cache between pools is memory-intensive; F5 offloads this management

⚖️ Load Balancing

Balancing work across prefill/decode pools requires CPU coordination that F5 can offload

🔄 Coordination Overhead

The +5% CPU overhead from disaggregation is recovered through F5's CPU offload capabilities

Disaggregated F5 Boost = Base_F5_Boost × 1.12
// Applied when "Disaggregated" toggle is enabled in sidebar

📚 Research Reference:

Splitwise: Disaggregated LLM Serving (arXiv:2401.02451) — Describes how separating prefill and decode phases improves serving efficiency, which F5 DPU further enhances through network and memory offload.

💼 OpEx Savings Calculation

What This Measures: Operational expenditure savings from F5 DPU deployment, including reduced orchestration complexity, lower management overhead, simplified networking, and operational streamlining.

OpEx_Savings = GPU_Count × Base_OpEx × Reduction% × Workload_Factor × Model_Factor × Ratio_Efficiency

Component	Default	Range	Description
Base OpEx ($/GPU/year)	$1,000	$100 - $5,000	Annual operational cost per GPU including: • Management & orchestration overhead • Network operations • Monitoring & observability • Incident response
F5 Reduction %	15%	5% - 40%	Percentage of OpEx reduced by F5 DPU: • Simplified orchestration • Reduced network complexity • Automated traffic management • Lower debugging overhead
Workload Factor	0.9 - 1.5×	By workload type	Complex workloads (MoE, Multi-Agent) have higher orchestration overhead → more savings potential
Model Factor	0.22 - 2.75×	By model size	Larger models require more complex orchestration → more savings from F5 simplification
Ratio Efficiency	0 - 1.0×	min(1.0, 8/ratio)	Diminishing returns above 8:1 GPU:DPU ratio. Denser deployments get more DPU leverage.

⚠️ Transparency Notice:

The default $1,000/GPU/year Base OpEx is an estimate
The default 15% reduction rate is an estimate
These values are NOT sourced from specific F5 benchmarks
Actual OpEx varies significantly by organization, team size, and operational maturity
Both values are configurable in the sidebar — adjust based on your actual figures

Example Calculation (1000 GPUs, 70B model, Multi-Agent workload, 8:1 ratio):

OpEx_Savings = 1000 × $1,000 × 0.15 × 1.4 × 1.30 × 1.0
= $273,000/year

🏗️ Base Utilization by Neocloud Maturity Stage

Why Maturity Matters: Base GPU utilization varies dramatically based on operational maturity. Emerging neoclouds often struggle with 40-60% utilization due to bursty demand and immature orchestration—this is where F5 DPU provides the most transformative value.

Stage	Typical Util	Characteristics	F5 DPU Value Focus
🚀 Emerging	40-60%	Bursty customer demand Manual/basic orchestration Inconsistent batch scheduling GPU clusters idle between jobs	Transformation story: "Reach 75%+ utilization faster" Massive capacity unlock Highest ROI potential
📈 Growing	60-75%	Stabilizing customer base Improving orchestration (Kubernetes, Slurm) Some workload diversity Still significant headroom	Acceleration story: "Path to 85%+ efficiency" Strong capacity gains Very good ROI
🏢 Established	80-90%	Mature operations Sophisticated scheduling Optimized batching Limited headroom for capacity gains	Optimization story: Focus on: • Latency (11× TTFB improvement) • Throughput (+20-30% tokens/sec) • Operational simplification

💡 ROI Implication

Same F5 investment, different value story: An emerging neocloud at 42% utilization might see 200%+ ROI from capacity recovery, while an established neocloud at 85% might see 50% ROI—but their value comes from latency, throughput, and operational benefits rather than capacity. Both are valid use cases. The calculator adapts to show the appropriate value proposition for each maturity stage.

🔓 GPU Capacity Freed Calculation

Key Concept: The utilization improvement from F5 DPUs frees up equivalent GPU capacity that can be repurposed for additional workloads, training jobs, or as burst headroom.

GPUs_Freed = GPU_Count × (Utilization_Boost ÷ Base_Utilization)
GPU_Hours_Freed = GPUs_Freed × 8,760 hours/year
Gross_Capacity_Value = GPU_Hours_Freed × GPU_Hourly_Rate
Realized_Capacity_Value = Gross_Capacity_Value × Realization_Rate

Why divide by Base Utilization? This answers: "How many additional GPUs would I need WITHOUT F5 to achieve the same output?" For example, if F5 improves utilization from 85% → 92% on 256 GPUs, that's equivalent to having 21 additional GPUs at the original 85% utilization (256 × 7% ÷ 85% = 21).

💰 Tiered Capacity Realization Rates

Critical Assumption: Not all freed GPU capacity translates directly to revenue. The realization rate accounts for demand constraints, ramp-up time, and market conditions. We tier this by neocloud maturity:

Maturity Stage	Realization Rate	Rationale
🚀 Emerging (40-60% util)	20%	Already underutilized due to low demand. Freed capacity = growth runway, not immediate revenue. Customer acquisition takes time.
📈 Growing (60-75% util)	35%	Stabilizing demand with growing customer base. Mix of immediate monetization and growth runway.
🏢 Established (80-90% util)	60%	High demand, often capacity-constrained. Waiting lists, premium pricing. Can immediately monetize freed capacity.

Example (Emerging, 1000 H100s, Standard mode):
• 333 GPUs freed × 8,760 hrs × $3.50/hr = $10.22M gross
• At 20% realization: $10.22M × 20% = $2.04M realized value (included in ROI)

📊 Live Example (from your current configuration)

Loading calculation from dashboard...

📈 GPU Hourly Rate Reference (Dec 2025 Market Rates)

GPU	$/hr	GPU	$/hr	GPU	$/hr
V100-16	$1.20	A100-80	$2.80	H200	$4.50
V100-32	$1.50	H100	$3.50	B100	$5.50
A100-40	$2.20	H100-NVL	$3.20	B200/GB200	$6.50-$8.00

Rates based on CoreWeave, Lambda Labs, and major cloud provider pricing as of December 2025. On-demand rates; reserved/committed pricing typically 30-50% lower.

💡 How to Interpret GPU Capacity Freed

GPUs Freed: The equivalent number of additional GPUs you would need to purchase WITHOUT F5 to achieve the same total output. This represents the capacity gain from improved utilization.
GPU-Hours/Year: Total compute time freed annually. Use this for capacity planning and workload scheduling.
Capacity Value: The economic value of the freed capacity, priced at market GPU rental rates. This represents potential additional revenue or avoided GPU procurement costs.

📉 Understanding Diminishing Returns at Higher Base Utilization

The "GPUs Freed" metric exhibits diminishing returns as base utilization increases. This is mathematically correct and economically meaningful.

Base Util	F5 Util	Boost	GPUs Freed (256 GPUs)	F5 Value Focus
75%	85%	+10 pts	34.1 GPUs	Capacity recovery (high waste to capture)
85%	92%	+7 pts	21.1 GPUs	Balanced (capacity + performance)
90%	95%	+5 pts	14.2 GPUs	Latency & throughput improvements
95%	98%	+3 pts	8.1 GPUs	Operational simplification & latency

Why This Matters for ROI Conversations:

Low utilization (60-80%): Lead with capacity recovery story—F5 "unlocks" significant GPU equivalents
Medium utilization (80-90%): Balanced pitch—capacity gains plus performance improvements
High utilization (90%+): Lead with latency (11× TTFB), throughput (+20-30%), and operational benefits—capacity gains are secondary

📚 Sourced Benchmarks: F5 BIG-IP + NVIDIA BlueField

Active (Standard): CPU Offload: 70% | Token Throughput: +20% | TCO Reduction: 17.8% | Power: -24% | Networking Savings: 30%

📊 Parameter Calibration by Mode (Updated December 2025)

All parameters are derived from published benchmarks, vendor testing, and analyst reports. Click any source link for detailed methodology.

Metric	🚀 Aggressive	⚡ Standard	🔒 Conservative	Primary Source
CPU Offload	99%	70%	30%	F5/SoftBank PoC (July 2025); Red Hat BF-2; VMware vSphere 8
GPU Util Improvement	+50%	+30%	+15%	PIPO research (2025): 40%→90% GPU utilization
TTFB Reduction	91% (11×)	60%	30%	F5/SoftBank PoC: 11× TTFB improvement on H100 cluster
Token Throughput	+30%	+20%	+10%	F5 NCP Architecture Blog (Oct 2025)
TCO Reduction	30%	17.8%	10%	NVIDIA 10K server study (Nov 2022): $148M→$121.7M
Power Reduction	34%	24%	15%	NVIDIA VMware (34%); Ericsson 5G UPF (24%); NREL (15%)
Networking Savings	40%	30%	15%	F5 infrastructure consolidation; Red Hat BF-2 testing
Util Boost Base	7.0%	5.0%	3.0%	Calibrated from CPU offload + throughput research
Minimum Floor	4.0%	2.5%	1.5%	Guaranteed minimum benefit from DPU offload

Key Sources:

F5/SoftBank PoC (July 2025): BIG-IP + BlueField-3 on H100 cluster - 99% CPU offload, 11× TTFB, 190× energy efficiency
NVIDIA DPU Power Efficiency (Nov 2022): 10K server study - $26.6M savings (17.8% TCO reduction)
MangoBoost MLPerf v5.0 (Apr 2025): 103K tokens/sec on 32× MI300X with DPU acceleration
Red Hat BlueField-2: 70% CPU reduction, IPsec at 100 Gbps line rate

🧠 Model Size Parameters

📊 Active Mode: Standard (full comparison below)

Model	Tok/sec	🚀 Aggressive		⚡ Standard		🔒 Conservative		$/1M
Model	Tok/sec	F5×	ROI*	F5×	ROI*	F5×	ROI*	$/1M

* ROI calculated with current settings: 256 GPUs, 8:1 ratio, realtime workload, disaggregated architecture
📈 Scale Factor: 1.00× (economies of scale applied at larger deployments)

📈 Economies of Scale Tiers

GPU Count	Scale Factor	Rationale
<128	0.95× - 1.00×	Small: Overhead inefficiency
128 - 256	1.00×	Entry enterprise: Baseline
256 - 512	1.00× - 1.08×	Mid-size: Modest efficiency
512 - 1,024	1.08× - 1.18×	Large: Operational leverage
1,024 - 2,048	1.18× - 1.30×	Enterprise: Volume discounts
2,048 - 4,096	1.30× - 1.45×	Hyperscale: Significant leverage
>4,096	1.45× - 1.60×	Mega-scale: Max benefits (capped)

Scale factors reflect operational leverage, F5 volume licensing, and infrastructure efficiency gains at larger deployments.

🚀 Aggressive

Peak performance envelope
Expert-tuned infrastructure
Maximum batch concurrency
Long context + high KV pressure
For opportunity sizing

⚡ Standard DEFAULT

Upper performance envelope
Well-optimized deployments
High batch concurrency
Production workloads
For planning & budgeting

🔒 Conservative

Baseline F5 benefits
Initial deployment estimates
Risk-averse planning
Early-stage / PoC
For CFO approval

🖥️ NVIDIA Data Center GPU Specifications (Dec 2025)

GPU	VRAM (GB)	Bandwidth (TB/s)	TDP (W)	Architecture	1× GPU	2× GPU	4× GPU	8× GPU
V100-16	16	0.9	300	Volta	7B	13B	32B	70B
V100-32	32	0.9	300	Volta	13B	32B	70B	175B
A100-40	40	1.6	400	Ampere	13B	32B	70B	175B
A100-80	80	2.0	400	Ampere	32B	70B	175B	405B
H100	80	3.35	700	Hopper	32B	70B	175B	405B
H200	141	4.8	700	Hopper	70B	175B	405B	671B
B100	192	8.0	700	Blackwell	70B	175B	405B	671B
B200	192	8.0	1000	Blackwell	70B	175B	405B	671B
GB200	384	16.0	1000	Grace-Blackwell	175B	405B	671B	671B×2
GB300	288	16.0	1200	Grace-Blackwell	175B	405B	671B	671B×2
R200 NEW	288	22.0	1800	Rubin (2H 2026)	175B	405B	671B	1T+
R200-Ultra 2027	1024	44.0	2200	Rubin-Ultra (2027)	405B	671B	1T+	2T+

Max Model Capacity: FP16 weights + KV cache at 65% utilization. Multi-GPU requires NVLink/NVSwitch for tensor parallelism. ■ 405B capable ■ 671B+ capable (DeepSeek/Llama 4 scale)

⚡ NVIDIA Vera Rubin (R200) - Power & Thermal Considerations

R200 Specifications:

TDP: 1,800W (2.6× H100)
Memory: 288GB HBM4 @ 22 TB/s
Compute: 50 PFLOPS FP4 inference
Cost: ~$200K+ estimated
Availability: 2H 2026

Thermal Requirements:

Liquid cooling mandatory
NVL72 rack: ~120-130kW total
8× perf/watt vs Blackwell (inference)
10× lower cost per token vs Blackwell
R200-Ultra (2027): 2,200W, 1TB HBM4e

📋 Workload Classification

Standard

Basic Queries
Batch Inference
Real-Time Inference

Lower CPU overhead (20-35%)

Advanced

RAG Pipeline
Test-Time Compute
MoE Models
Synthetic Data Gen

Higher orchestration overhead (35-55%)

Agentic

AI Agents
Multi-Agent Systems
Deep Reasoning (o1-style)

Highest F5 benefit (1.25-1.45×)

🔧 Workload × Model Impact Matrix

Expected F5 DPU ROI by workload type and model size. F5 benefit scales with orchestration complexity.

Workload	CPU Overhead	F5 Benefit	7B	8B	13B	32B	70B	175B	405B	671B
Basic Queries	25%	1.0×	-85%	-75%	-60%	-45%	-35%	-15%	+5%	+15%
Real-time Serving	30%	1.1×	-70%	-60%	-45%	-25%	-5%	+20%	+45%	+65%
RAG Pipeline	45%	1.3×	-55%	-45%	-25%	0%	+25%	+55%	+90%	+120%
Synthetic Data Gen	35%	1.15×	-65%	-55%	-35%	-15%	+10%	+35%	+65%	+90%
AI Agents	50%	1.35×	-50%	-40%	-15%	+10%	+35%	+70%	+110%	+145%
Multi-Agent	50%	1.4×	-45%	-35%	-10%	+15%	+45%	+85%	+130%	+170%
MoE Inference	55%	1.5×	-35%	-25%	0%	+30%	+67%	+115%	+165%	+210%
Test-Time Compute	55%	1.45×	-40%	-30%	-5%	+25%	+60%	+105%	+155%	+195%

7B-8B Negative ROI - F5 not recommended 13B-32B Marginal - workload dependent 70B+ Positive ROI - F5 sweet spot

🚀 Frontier Models (MoE Architectures 2025-2027)

Ultra-scale models use Mixture-of-Experts (MoE) architectures where only a subset of parameters (typically 5-30B) are activated per inference, dramatically reducing compute requirements while maintaining full model capability.

Model Class	Total Params	Active Params	Memory (FP16)	~$/1M Tokens	F5 ROI Factor	Examples
671B	671B	~37B	1.6 TB	$18	1.5-2.75×	DeepSeek-V3, Llama 4
1T	1 Trillion	~5-30B	2.4 TB	$25	1.8-3.1×	Gemini 3 Pro
2T	2 Trillion	~50-100B	4.8 TB	$40	2.1-3.5×	GPT-5 (est.)
5T	5 Trillion	~100-200B	12 TB	$75	2.4-4.0×	Grok 5 (est.)
10T	10 Trillion	~200-500B	24 TB	$150	2.7-4.5×	Future (2027+)
20T	20 Trillion	~500-800B	48 TB	$300	3.0-5.0×	AGI-Scale (2028+)

Key Insight: MoE architectures deliver frontier capability with dramatically lower inference cost. F5 DPU benefits increase with model scale due to more complex expert routing, KV cache management, and orchestration overhead. Models at 1T+ scale represent the highest F5 ROI opportunity.

📊 GPU:DPU Ratio Impact

Ratio	Util Boost	ROI (70B)	Notes
4:1	+27%	-28%	Over-provisioned (too many DPUs)
8:1	+27%	+45%	Optimal balance (recommended)
16:1	+19%	+97%	Higher ROI, reduced boost
32:1	+11%	+121%	Sparse - diminishing returns

⚙️ Key Assumptions

Infrastructure

H100 baseline: 700W, $30-40K/GPU
PUE: 1.4 (industry standard)
DPU power: 50W each
Electricity: $0.12/kWh default

Financial

Base $/token: $0.0000005
Discount rate: 12% default
License term: 3 years default
Utilization floor: 85%

🔧 DPU Hardware Cost Model

Purpose: Different procurement models exist for DPU hardware. This toggle determines whether DPU hardware is an additional capital expense or included with GPU infrastructure.

Model	Description	Year 0 Impact	Use Case
Bundled	DPU hardware included in GPU infrastructure package	$0 additional	New cluster deployments with integrated DPU, OEM partnerships
Add-on	DPU hardware purchased separately	DPUs × $3,800/unit	Retrofitting existing clusters, standalone DPU procurement

DPU Hardware Cost (Add-on) = DPU_Count × DPU_Unit_Price
Where: DPU_Count = GPU_Count ÷ GPU:DPU_Ratio
Default unit price: $3,800 (NVIDIA BlueField-3 market range: $3,600-$4,000)

💳 License Payment Model

Purpose: F5 licensing can be structured as upfront CapEx or annual subscription. This affects cash flow timing but not the ROI calculation, which uses annualized costs for comparability.

Model	Description	Year 0	Years 1-N
CapEx (Upfront)	Full multi-year license paid upfront	Annual × Term	$0
Subscription	Annual payments at start of each period	Year 1 sub	Next year's sub*

* Subscription payments are made at the start of each period (pay for Year 2 during Year 1, etc.). Last year has no payment.

📊 Cash Flow Examples (3-Year Term)

Example: 1000 GPUs, 8:1 ratio (125 DPUs), $10,000/year annual F5 license

💰 CapEx Model (Bundled DPU)

Year	Investment	F5 License	Benefits
Year 0	-$30,000	-$30,000	$0
Year 1	$0	$0	+Benefits
Year 2	$0	$0	+Benefits
Year 3	$0	$0	+Benefits

Key: All license cost upfront → higher NPV (no discounting on future payments)

📅 Subscription Model (Bundled DPU)

Year	Investment	F5 License	Benefits
Year 0	-$10,000	-$10,000	$0
Year 1	$0	-$10,000	+Benefits
Year 2	$0	-$10,000	+Benefits
Year 3	$0	$0	+Benefits

Key: Spread payments → lower initial outlay, payments at start of each period

🔧 Add-on DPU + Subscription (125 DPUs @ $3,800)

Year	DPU HW	F5 License	Total Outflow	Benefits	Net Cash Flow
Year 0	-$475,000	-$10,000	-$485,000	$0	-$485,000
Year 1	$0	-$10,000	-$10,000	+Benefits	Benefits - $10K
Year 2	$0	-$10,000	-$10,000	+Benefits	Benefits - $10K
Year 3	$0	$0	$0	+Benefits	Benefits only

Note: DPU hardware is a one-time Year 0 expense. Use Add-on model when retrofitting existing GPU clusters.

⚠️ Important:

ROI Calculation: Always uses annualized F5 cost regardless of payment model (for comparability)
Cash Flow Tab: Shows actual payment timing based on selected model
NPV/IRR: Currently uses simplified annual net benefit (future enhancement: payment-timing-aware NPV)

🎯 Break-Even Analysis

Minimum Viable Configuration

70B + RAG workload: ~25% ROI (break-even threshold)
70B + Basic workload: Negative ROI (not recommended)
175B + Basic workload: ~51% ROI (viable even for simple workloads)
Small models (<32B): Not recommended for F5 deployment

💎 Token-Based Pricing Models (Models 3 & 4)

Model 3: Per-Token Pricing

F5 charges a fraction of every token processed through its DPU infrastructure. This aligns F5's revenue directly with the customer's usage volume.

                F5 Annual Revenue = Total_Tokens/sec × 3600 × Active_Hours/yr × F5_Price/Token

                Customer Annual Revenue = Total_Tokens/sec × 3600 × Active_Hours/yr × Customer_Price/Token

                Value Gap = Customer Revenue − F5 Revenue (what customer keeps)

Where: Total_Tokens/sec includes the F5-enhanced throughput across the entire GPU fleet. Active_Hours = Operational Hours × Token Utilization %. Default F5 price ($0.015/1M tokens) represents ~1% of a typical blended market rate.

Model 4: Incremental Token Pricing

F5 charges only for the additional tokens/sec enabled by DPU deployment — the throughput uplift beyond baseline. This is the purest "pay for value" model.

                Incremental Tokens/sec = F5_Enhanced_Throughput − Baseline_Throughput

                F5 Annual Revenue = Incremental_Tokens/sec × 3600 × Active_Hours/yr × F5_Price/Token

                Customer Incr. Revenue = Incremental_Tokens/sec × 3600 × Active_Hours/yr × Customer_Price/Token

Key insight: This model directly ties F5's revenue to the measurable throughput improvement. If F5 DPUs provide a 30% throughput boost, F5 charges on that 30% incremental capacity only.

📊 Market Token Pricing Reference (March 2026)

Frontier Models ($/1M tokens)

GPT-4o	$2.50 in / $10.00 out
GPT-5.2	$1.75 in / $14.00 out
Claude Sonnet 4.6	$3.00 in / $15.00 out
Claude Opus 4.6	$5.00 in / $25.00 out
Gemini 2.5 Pro	$1.25 in / $10.00 out

Open-Source / Budget ($/1M tokens)

Llama 4 Maverick	$0.27 in / $0.85 out
Llama 70B (Groq)	$0.59 in / $0.79 out
Llama 70B (Together)	$0.90 blended
Claude Haiku 4.5	$1.00 in / $5.00 out
Gemini Flash	$0.50 in / $3.00 out

Prices are blended input/output rates sourced from provider APIs as of March 2026. F5's default rate ($0.015/1M tokens) is set at ~1% of a typical customer blended rate, representing the infrastructure-layer value capture.

📚 Data Sources & References

🖥️ GPU Hardware Specifications

NVIDIA H100/H200: NVIDIA H100 Tensor Core GPU - 80GB HBM3, 3.35 TB/s, 700W TDP
NVIDIA B100/B200: NVIDIA Blackwell Architecture - 192GB HBM3e, 8 TB/s bandwidth
NVIDIA GB200/GB300: NVIDIA Grace Blackwell Superchip - GTC 2024 announcement
NVIDIA Vera Rubin (R200): NVIDIA Rubin Platform - 288GB HBM4, 22 TB/s, 1800W TDP (2H 2026)
NVIDIA Rubin NVL72: Vera Rubin NVL72 - 5× inference perf, 10× lower cost/token vs Blackwell
Memory Bandwidth: NVIDIA Hopper Architecture Deep Dive
Tensor Core Generations: NVIDIA Ampere Whitepaper (PDF)

💰 GPU Pricing & Market Data

H100 SXM5 ($25-40K): Tom's Hardware H100 Pricing Analysis
Cloud GPU Hourly Rates: CoreWeave GPU Cloud Pricing - H100: $2.49-4.25/hr
Hyperscaler Rates: AWS P5 Instances | Azure ND Series
GPU Market Analysis: SemiAnalysis - Industry GPU economics reports

🏢 Neocloud Economics & Financials

IREN (Iris Energy): SEC EDGAR - IREN Filings - S-1/10-K for GPU cloud economics
CoreWeave: CoreWeave Blog - Infrastructure economics, investor presentations
Nebius: Nebius Investor Relations - GPU cloud unit economics
Lambda Labs: Lambda GPU Cloud - Pricing benchmarks

⚡ Token Pricing & Inference Benchmarks

OpenAI Pricing: OpenAI API Pricing - GPT-4o, GPT-4 Turbo rates
Anthropic Pricing: Anthropic API Pricing - Claude 3.5/4 rates
Together.ai: Together AI Pricing - Open model inference costs
Fireworks.ai: Fireworks Pricing - Serverless inference rates
MLPerf Inference: MLCommons Inference Benchmarks

💵 Market Token Pricing Reference Dec 2025

Reference rates from major inference providers. The calculator uses the Blended Average column by default. You can override with a custom rate in the sidebar.

Model Size	Together AI	Fireworks	Anyscale	Groq	Calculator Default
7-8B (Llama 3.1 8B)	$0.05	$0.05	$0.15	$0.05	$0.07-0.10
13B	$0.10	$0.10	$0.25	—	$0.15
32B (Qwen 32B)	$0.30	$0.40	—	—	$0.40
70B (Llama 3.1 70B)	$0.88	$0.90	$1.00	$0.59	$1.00
175B (GPT-3.5 class)	—	—	—	—	$5.00
405B (Llama 3.1 405B)	$3.50	$3.00	—	—	$12.00
671B (DeepSeek-V3)	$1.25	—	—	—	$18.00

Note: Prices shown are per 1M output tokens (input typically 50-80% cheaper). Calculator defaults are set higher than spot rates to reflect enterprise SLA pricing, burst capacity premiums, and self-hosted margin targets. DeepSeek-V3 pricing is anomalously low due to MoE efficiency—adjust upward for non-MoE frontier models.

Sources: Together AI | Fireworks | Anyscale | Groq • Last updated: December 2025

🔧 DPU Technology & Infrastructure

F5 BIG-IP Next: F5 BIG-IP Product Page
NVIDIA BlueField-3: NVIDIA DPU Overview
AMD Pensando: AMD Pensando DPU
Data Center PUE: Google Data Center Efficiency - Industry PUE benchmarks (1.1-1.5)

🧠 Model Architecture References

Llama 3.1 405B: Meta AI - Llama 3.1 Release
DeepSeek-V3 671B: DeepSeek-V3 Technical Report
MoE Architectures: Mixtral of Experts (arXiv)
KV Cache Optimization: PagedAttention (vLLM)
Disaggregated Serving: Splitwise: Disaggregated LLM Serving

📊 GPU-Model Capacity Table Notes

The 1×/2×/4×/8× GPU columns show per-server tensor parallelism requirements:

1× GPU: Single GPU inference (no parallelism needed)
2×/4× GPU: Tensor parallelism within a single multi-GPU node
8× GPU: Full 8-GPU server (e.g., DGX H100) with NVLink/NVSwitch
Memory calculation: FP16 weights (~2 bytes/param) + 65% KV cache utilization
Multi-node: For models exceeding 8× GPU capacity, requires NVSwitch fabric or pipeline parallelism

Last Updated: December 2025 | Data sources verified as of publication date. GPU pricing and cloud rates subject to market conditions.

⚠️ Disclaimer

This calculator provides estimates for planning purposes only. Actual ROI will vary based on specific workloads, infrastructure configurations, market conditions, and operational factors. Consult with F5 sales engineering for detailed assessments.

📎 Appendix

Detailed NPV Calculation (256 GPUs)

Initial Investment: -$2.56M
Year 1 Savings: $X.XXM (GPU depreciation + opex)
Year 2 Savings: $X.XXM
Year 3 Savings: $X.XXM

              NPV = -I + CF₁/(1+r) + CF₂/(1+r)² + CF₃/(1+r)³
            
              NPV = -2.56 + X/1.12 + X/1.25 + X/1.40
            
              NPV = -2.56 + X + X + X = $X.XXM

* Discount rate: 12% | License term: 3 years | Discount factors: 1.12, 1.25, 1.40

Total Cost of Ownership (TCO) per Token

❌ Without DPU:

• GPU depreciation: $0.08/1M tokens

• Power & cooling: $0.02/1M tokens

• Operations: $0.02/1M tokens

Total: $0.12/1M tokens

✅ With F5 DPU:

• GPU depreciation: $0.05/1M tokens

• Power & cooling: $0.015/1M tokens

• Operations: $0.015/1M tokens

• DPU license: $0.01/1M tokens

Total: $0.09/1M tokens

💰 TCO Savings: 25% per token with F5 DPU ($0.03/1M tokens reduction)

Scenario A (Current)

Scenario B (Optimized)

⚡ Quick Presets

🏢 Storage Vendor

🔗 Interconnect

💰 Storage Infrastructure Cost (CapEx)

Model Overview

📐 Core ROI Formula

🧪 Tolly Report #226104 — Separated Value Streams (March 2026)

🔧 CPU Offload Breakdown

🏭 GPU Cloud Economics Methodology

Current GPU Types

Add/Edit GPU

Current Models

Add/Edit Model

💾 F5 + Context-Aware Storage Integration NEW

⚡ Quick Presets

🏢 Storage Vendor

🔗 Interconnect Technology

📊 F5 + Storage Synergy Impact

🌐 Remote Configuration Sync

📤 Export Configuration

📥 Import Configuration

🔄 Reset to Defaults

🖥️ GPU Requirements Matrix

🏢 Deployment Scale Matrix

⚡ F5 Utilization Boost Calculation

💾 NVIDIA Inference Context Memory Storage Platform (ICMS)

🔷 NVIDIA BlueField-4 DPU Specifications

📊 ICMS Verified Performance Metrics

🤝 Confirmed NVIDIA ICMS Partners (CES 2026)

🔧 Key ICMS Technology Components

💰 Incremental Token Revenue Calculation

🏋️ Training Value Calculation

🔀 Disaggregated Serving Bonus

💼 OpEx Savings Calculation

🏗️ Base Utilization by Neocloud Maturity Stage

🔓 GPU Capacity Freed Calculation

📚 Sourced Benchmarks: F5 BIG-IP + NVIDIA BlueField

🧠 Model Size Parameters

🖥️ NVIDIA Data Center GPU Specifications (Dec 2025)

⚡ NVIDIA Vera Rubin (R200) - Power & Thermal Considerations

📋 Workload Classification

Standard

Advanced

Agentic

🔧 Workload × Model Impact Matrix

🚀 Frontier Models (MoE Architectures 2025-2027)

📊 GPU:DPU Ratio Impact

⚙️ Key Assumptions

🔧 DPU Hardware Cost Model

💳 License Payment Model

📊 Cash Flow Examples (3-Year Term)

🎯 Break-Even Analysis

💎 Token-Based Pricing Models (Models 3 & 4)

Model 3: Per-Token Pricing

Model 4: Incremental Token Pricing

📊 Market Token Pricing Reference (March 2026)

📚 Data Sources & References

🖥️ GPU Hardware Specifications

💰 GPU Pricing & Market Data

🏢 Neocloud Economics & Financials

⚡ Token Pricing & Inference Benchmarks

💵 Market Token Pricing Reference Dec 2025

🔧 DPU Technology & Infrastructure

🧠 Model Architecture References

📊 GPU-Model Capacity Table Notes

📎 Appendix

Detailed NPV Calculation (256 GPUs)

Total Cost of Ownership (TCO) per Token