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Benchmarking Local AI Models

by ailona•

Guide to building a reproducible benchmarking platform for local AI models using Foundry Local, FLPerformance, Node.js and React. Explains scientific measurement, multi-dimensional metrics (TTFT, TPOT, latency, throughput), hardware and concurrency effects, and statistical rigor for model selection.

Opening paragraph: Benchmarking local AI models now includes a reproducible platform using Foundry Local and FLPerformance. This brings scientific measurement to model evaluation on real hardware.

Main feature/change and impact

The platform formalizes controlled benchmarking for local models with orchestration, measurement, and visualization. It loads models into Foundry Local, runs configured suites, and records TTFT, TPOT, total latency, throughput, and error rates. Aggregated statistics include mean, p50, p95, and p99. This change moves model selection from anecdote to data-driven decisions on latency and quality tradeoffs.

Practical implications

Teams can test models on target hardware and realistic workloads before deployment. The system supports warmups, concurrency, and streaming measurements for accurate TTFT metrics. Results persist to JSON for auditability and comparison over time. Developers can validate latency budgets, memory fit, and concurrent performance for production SLAs and cost projections.
“Scientific benchmarking demands controlled conditions, statistically significant sample sizes, multi-dimensional metrics, and reproducible methodology.” “You need dozens or hundreds of trials to establish p50, p95, p99 percentiles, understand variance, and detect stability issues.”
Closing paragraph: Adopt the platform to verify model choices against your latency and hardware constraints. Next steps include adding bespoke prompt suites, automating nightly runs, and integrating results into CI for regression detection.

Key points from the article:

  • Run benchmarks on target hardware, not cloud leaderboards.
  • Measure TTFT, TPOT, total latency, throughput, and error rates.
  • Use warmup iterations and controlled concurrency for stability.
  • Collect p50/p95/p99 percentiles to capture performance distribution.
  • Automate load/unload and identical workloads for fair comparisons.

    • Related Coverage:

    From the Microsoft Developer Community Blog articles