Show HN: Llmspeed – feel how fast a local LLM runs before you buy the hardware

llmspeed is an interactive simulator that shows how responsive a local large language model will feel on different hardware before committing to a purchase. It presents a searchable matrix of specific GPUs, CPUs, Macs, and cloud accelerators (from RTX 3090 and 4090 through M5 Ultra, DGX Spark, H100/A100/H200 clouds and more) alongside dozens of open models (Qwen, Gemma, Llama 3.1, GPT-OSS, Ornith, DeepSeek and others). For a short prompt on a single device it reports latency estimates so users can compare expected inference speed across hardware and model combinations. The tool runs in the browser (requires JavaScript) and the code and data are available on GitHub.

Reported numbers come from community benchmarks (r/LocalLLaMA, localscore.ai and similar) when available; other entries are projected from comparable setups or vendor/spec extrapolations. The site distinguishes measured versus derived values and focuses on single-device, short-prompt performance rather than sustained or multi-node throughput. That specificity makes it practical for anyone choosing a workstation or GPU for local experimentation, giving concrete, open-source-backed estimates to guide hardware selection while making clear which figures are community-measured and which are projections.