Introducing Chip Benchmark: Hardware-Centric Performance Insights for AI Workloads

As the AI hardware ecosystem rapidly expands, choosing the right accelerator for a given workload has become increasingly complex. Different chips excel in different scenarios — but making apples-to-apples comparisons remains difficult without standardized, open tooling.

We're excited to introduce Chip Benchmark, an open-source benchmarking suite purpose-built to evaluate the performance of open-weight LLMs across diverse hardware platforms. Chip Benchmark supports NVIDIA A100/H100/L40S and AMD MI300X — with upcoming plans to include other hardware vendors and models.

Built for Transparency

We built Chip Benchmark for reproducibility and easy comparison. With an open-source scripting available here, it runs standardized tests across different hardware, logging results in both human and machine-readable formats.

It measures key metrics like throughput, latency, and time-to-first-token across a range of sequence lengths and concurrency levels. Results are organized by model, hardware, and precision for clear, system-level insights.

Dashboard Insights

Alongside the benchmarking scripts, we offer an interactive web-based dashboard to visualize results. Users can filter by model, hardware, and precision, and view detailed throughput and latency comparisons.

In the example shown, throughput curves for Llama-3.1-8B-Instruct reveal that while both the H100 and MI300X scale with concurrency, the H100 demonstrates stronger throughput at higher levels. This kind of insight is critical for informed hardware selection, especially at deployment scale.

Get Involved

We welcome contributors, hardware vendors, and researchers. Find the repository here and let's benchmark the future - together.

Want to see a specific benchmark? Request a benchmark or sign up for notifications (hit the bell icon in the top right) to stay updated as we add new hardware and results!