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GPU & Cluster Benchmarking
MLPerf-style training and inference benchmarks across NVIDIA, AMD and cloud instances — plus fabric stress tests (NCCL all-reduce, congestion behavior) that reveal what single-node numbers hide.
What We Benchmark
Measured. Reproducible. Independent.
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LLM Inference Benchmarking
Tokens per second, time-to-first-token, latency under concurrency and cost per million tokens — across serving stacks (vLLM, TensorRT-LLM, Triton) and quantization levels.
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LLM Quality Evaluation
Task-specific eval suites for your use case — accuracy, hallucination rates, safety behavior — comparing commercial APIs against open-weight models on your real data.
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Inference Cost Optimization
Batching, quantization, KV-cache tuning, model routing and right-sizing. Typical engagements find 40–70% cost reduction on production inference workloads.
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Pre-Purchase Validation
About to commit to a GPU order or a multi-year cloud deal? We benchmark your candidate configurations on rented capacity first, so the PO reflects evidence.
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Capacity Planning
Workload modeling that translates business growth into GPU counts, power and budget — so you buy the right amount of compute at the right time.
Deliverables
A Report You Can Take to the Board
Every benchmarking engagement produces a reproducible methodology, raw data, and an executive summary that ranks your options by performance-per-dollar for your specific workload. Where we find optimization headroom, we quantify it and — if you want — implement it.
- Independent. No hardware reseller margins, no cloud partnership quotas.
- Your workload, not synthetic only. We benchmark with your models, prompts and traffic patterns.
- Reproducible. Full methodology and scripts handed over — rerun it anytime.
- Actionable. Findings map directly to procurement and tuning decisions.
How Much Is Unmeasured AI Costing You?
If you're spending five figures a month or more on AI compute, a benchmarking engagement almost always pays for itself. Let's find out.
Talk to a Performance Engineer