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.
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The Problem
A new GPU generation every six months - and most AI infrastructure is still bought off the spec sheet, without a single measurement.
01
It came from public ratings. Whether a cheaper model does the same job on your data - nobody checked.
02
A few wasteful operations eat 60-80% of spend - and stay invisible without profiling.
03
Proper tuning makes the same GPUs 2-5x faster. Most teams never try.
04
What the hardware truly costs over three years, nobody calculated. The gap is millions.
The Solution
AI Performance Labs is a PFLB service that measures the performance and cost of your AI infrastructure on dedicated GPU clusters. Pick any combination of six tracks:
What hardware your AI product needs today and at a three-year horizon: a minimum, an optimum, and a ceiling under your SLOs.
Models compared on the same hardware and your data: throughput, latency, answer quality, and cost per million tokens.
Where token spend is architecturally expensive - oversized context, repeated calls - and what fixing it saves, in tokens and dollars.
vLLM, TensorRT-LLM, SGLang, TGI; quantization, KV-cache, continuous batching. 2-5x throughput on the hardware you already run.
Buy, rent, or hybrid over 36 months: CapEx against OpEx, depreciation curves, delivery times, and the risk of skipping a generation.
The rack profile your configuration demands: kilowatts, cooling, placement density - and a data-center preparation plan.
The Labs
How It Works
What You Get
01
Methodology, measurement results, charts, and conclusions. Reproducible test conditions.
For the engineering team
02
A three-year procurement plan in dollars: configurations, timelines, and buy / rent / hybrid scenarios.
For the CIO and CFO
03
The serving stack configuration and parameters, with the rationale behind every choice.
For DevOps and SRE
Sample Measurements
Throughput, latency, concurrency - measured on your scenarios. From real PFLB reports:
| GPU SKU | TPS | TTFT | Concurrency |
|---|---|---|---|
| H100 80GB ×8 | 4 200 | 310 ms | 64 |
| H200 141GB ×8 | 6 800 | 240 ms | 96 |
| B200 ×8 | 14 500 | 150 ms | 180 |
| B300 ×8 | 19 200 | 120 ms | 240 |
The recommendation for this client: minimum H200 ×8, optimum B200 ×8, ceiling B300 ×16.
3.3× 4 200 → 13 800 tokens per second
Llama-70B: default vLLM against vLLM with FP8 and continuous batching. No hardware changes.
A few operations usually create 60-80% of all token spend.
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.
4 engagements, 4 successful launches, zero SAT-day outages
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.
Critical bug found 48 hours pre-launch - $2M revenue protected
We reply within one business day.