AI Performance Labs:
Test Before You Invest

We assemble a dedicated GPU lab around your product and measure what your AI actually needs: which hardware, which model, which serving stack, and what it costs over three years. You get numbers that hold up in front of a budget committee, not vendor slides.

5.0

Rating: 5 out of 5 stars based on Clutch reviews

Trusted by:

  • Moody's
  • KFC
  • Tinder

The Problem

Millions Spent on a Guess

A new GPU generation every six months - and most AI infrastructure is still bought off the spec sheet, without a single measurement.

  • 01

    The model was picked, not tested for performance

    It came from public ratings. Whether a cheaper model does the same job on your data - nobody checked.

  • 02

    The AI bill grows faster than the users

    A few wasteful operations eat 60-80% of spend - and stay invisible without profiling.

  • 03

    The hardware runs at a fraction of its power

    Proper tuning makes the same GPUs 2-5x faster. Most teams never try.

  • 04

    The budget comes from a price list

    What the hardware truly costs over three years, nobody calculated. The gap is millions.

The Solution

We Measure. You Decide.

AI Performance Labs is a PFLB service that measures the performance and cost of your AI infrastructure on dedicated GPU clusters, run by the same engineers behind our load and performance testing services. For how we drive a load test with an AI assistant end to end, see our step-by-step AI-driven load testing guide. Pick any combination of six tracks:

  • Hardware Sizing

    What hardware your AI product needs today and at a three-year horizon: a minimum, an optimum, and a ceiling under your SLOs.

  • LLM Benchmarking

    Models compared on the same hardware and your data: throughput, latency, answer quality, and cost per million tokens.

  • AI Token Cost Optimization

    Where token spend is architecturally expensive - oversized context, repeated calls - and what fixing it saves, in tokens and dollars.

  • Inference Stack Tuning

    vLLM, TensorRT-LLM, SGLang, TGI; quantization, KV-cache, continuous batching. 2-5x throughput on the hardware you already run.

  • GPU Procurement & TCO

    Buy, rent, or hybrid over 36 months: CapEx against OpEx, depreciation curves, delivery times, and the risk of skipping a generation.

  • Power & Cooling Envelope

    The rack profile your configuration demands: kilowatts, cooling, placement density - and a data-center preparation plan.

The Labs

Dedicated GPU Clusters. No Neighbors. No Virtualization.

  • 32×H100 / H200 SXM
    in one cluster
  • B2008-GPU SXM host,
    B300 on request
  • 64+GPU clusters,
    up to hundreds
  • 3.2 Tbit/sQuantum-2 InfiniBand
    per host

Deployed in hours

no queue
  • Up to 32× NVIDIA H100 / H200 SXM - four 8-GPU hosts
  • NVIDIA B200 SXM - an 8-GPU host
  • Smaller configurations: RTX PRO 6000, L40S

On request

1-2 weeks
  • NVIDIA B300 SXM (Blackwell Ultra)
  • Clusters of 64+ GPUs, NVLink in the host, eight ConnectX-7 NICs

How It Works

From a Call to a Measured Decision

  1. Discovery Call

    60 minutes. You describe the task - we propose the lab setup and the tracks that fit.

    Talk to Sales

  2. Estimate and Start

    We prepare the estimate, sign the documents, and reserve the hardware. Time and materials.

  3. Lab and Testing

    Your scenarios - chat, RAG, agentic tool-calls - on dedicated hardware. Typically 4-8 weeks.

  4. Decision Documents

    Three documents your teams act on: the report, the procurement plan, the playbook.

What You Get

Three Documents. Three Audiences.

  • 01

    Technical report

    Methodology, measurement results, charts, and conclusions. Reproducible test conditions.

    For the engineering team

  • 02

    Procurement plan

    A three-year procurement plan in dollars: configurations, timelines, and buy / rent / hybrid scenarios.

    For the CIO and CFO

  • 03

    Operational playbook

    The serving stack configuration and parameters, with the rationale behind every choice.

    For DevOps and SRE

Sample Measurements

What the Measurements Look Like

Throughput, latency, concurrency - measured on your scenarios. From real PFLB reports:

Hardware sizing · Llama-70B · 4K context · 8-GPU host
GPU SKUTPSTTFTConcurrency
H100 80GB ×84 200310 ms64
H200 141GB ×86 800240 ms96
B200 ×814 500150 ms180
B300 ×819 200120 ms240

The recommendation for this client: minimum H200 ×8, optimum B200 ×8, ceiling B300 ×16.

Inference stack tuning · same 8-GPU host

3.3× 4 200 → 13 800 tokens per second

Llama-70B: default vLLM against vLLM with FP8 and continuous batching. No hardware changes.

Token spend by operation · profiling example
  • RAG - oversized context36%
  • Agentic tool-calls22%
  • Repeated model calls16%
  • Chat history replay14%
  • System prompt10%

A few operations usually create 60-80% of all token spend.

Frequently Asked Questions

Do we need our own GPU hardware to start?
No. Testing runs in PFLB labs on dedicated H100, H200, and B200 hosts. You provide the model, a representative dataset, and the load scenarios; the report includes reproducible test conditions so the results transfer to your environment.
Which models can you test?
Open-source and proprietary models from 7B to 100B+ parameters, dense and MoE, including your fine-tunes. Quality is scored on a representative dataset from your product, not on public leaderboards.
How is the work priced?
Time and materials. The cost is made up of team time, hardware rental for the test period, and PFLB licenses for LLM load testing. The scope is any combination of the six tracks; a typical project runs 4-8 weeks, and we report by team hours and machine time.
What do we get at the end?
Three documents: a technical report with methodology, measurements, and conclusions for the engineering team; a three-year procurement plan in dollars with buy, rent, and hybrid scenarios for the CIO and CFO; and an operational playbook with the serving stack configuration and the rationale for every choice, for DevOps and SRE.
Which metrics does LLM inference benchmarking actually report?
Four that decide the deployment, measured under your own load rather than on a single prompt. Time to first token, which is what a user feels as responsiveness. Inter token latency, also written as time per output token, which sets how fast the answer streams. Throughput in output tokens per second across all concurrent requests. And the maximum number of concurrent users the configuration serves before latency constraints break. We report the whole curve, because inference performance is a trade: raising the maximum batch size lifts throughput and pushes total latency up at the same time.
How do you work out GPU memory requirements for a model?
Memory footprint is model weights plus KV cache plus overhead. The weights follow model size and quantisation; the KV cache calculation follows model parameters, sequence length and how many requests run at once, and it is the part teams underestimate. At a long context window the KV cache size can exceed the weights themselves, so the maximum context length you promise users is a hardware decision, not a product one. Grouped query attention shrinks it, tensor parallelism spreads it across multiple GPUs. We measure the real numbers on the target hardware instead of trusting a spreadsheet.
Why is LLM inference memory bound rather than compute bound?
Because token generation reads the whole set of model weights for every output token. That makes memory bandwidth, not raw compute, the ceiling for most inference systems: the GPU spends its time moving key value data rather than multiplying. It explains results that look wrong on paper, such as a larger models configuration running acceptably while a smaller one stalls, and why an inference engine change or a quantisation step can lift inference speed more than new hardware. It also explains why GPU utilization can read high while real world performance is poor.
What goes into AI infrastructure TCO besides the hardware?
The hardware purchase price is usually under half of it. The other cost categories: power consumption and the cooling requirements that follow from power density, which decide whether an entire rack fits your data centre at all; networking and data transfer; the software stack and support; and personnel costs, the line most three-year models omit. Over 36 months the total cost of a configuration that looked cheaper on the price list routinely ends up higher. That is why the procurement plan is a separate document from the technical report.
Can you tune the serving stack instead of buying more GPUs?
Often, yes, and it is the first thing worth trying. Custom benchmarking of your own scenarios finds the optimal configuration for the inference engine you already run: continuous batching, quantisation, tensor parallelism across multiple GPUs, and the key parameters that govern how multiple prompts share a batch. In one engagement the same hardware went from 4,200 to 13,800 output tokens per second on Llama-70B. We identify bottlenecks first and recommend hardware only when the software stack has nothing left to give.
What load profile do you run for LLM performance benchmarking?
Yours, not a synthetic one. The key factors are the shape of the input prompt and the output length you actually serve: a RAG answer with a 8k input sequence length behaves nothing like a short chat turn, and results from one deviate significantly from the other. We drive multiple requests concurrently at the concurrency levels you expect, hold each level long enough to read steady numbers, and push past it to find the upper bound. Where a low latency guarantee exists, we report where it breaks rather than the average, because the average hides the users who left.
How do you choose the final LLM deployment configuration?
By putting the key metrics next to the money. For each candidate configuration we have measured throughput, latency at your concurrency, resource utilization across the GPUs, and the computational overhead the serving stack adds. Cost efficiency is then cost per million tokens at the latency you promised, not at the best number the hardware can show. A large language model that needs two more GPUs to hit the same SLO is a different purchase, and that difference is what the procurement plan prices over three years.
Why not rely on public benchmarks?
Public leaderboards measure averages on general tasks. On a specific product the picture differs: one model is stronger in your domain terminology, another in your language, a third in long context. We compare models on the same hardware and your data, so the choice is grounded in your workload.

Updated 28 Aug 2026 — hardware list, cluster configurations and pricing reviewed.

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Don't Take Our Word
For It. Take Theirs.

Rating: 5 out of 5 stars

I was highly satisfied working with PFLB. They’re a fantastic one-stop shop for software performance, with everything you need: their platform and skilled engineers. I’ve used them four times to performance test the College Board software before big updates, and it all went smoothly. Plus, I love that they store all the testing data, so we can track performance capacity over time.

Bob Burke

Bob Burke

President, Folderwave

Rating: 5 out of 5 stars

It was a pleasure working with PFLB, and I’d recommend them for performance testing. They quickly got up to speed with our complex software architecture and were super productive in setting up our performance test environment in the cloud. They were really dedicated to improving our product with their strong expertise in performance optimization.

Steve Opel

Steve Opel

Principal Technology Manager, NOV CTES

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