Show HN: Llmspeed – feel how fast a local LLM runs before you buy the hardware
- ID
- 36662
- Status
- summarized
- Published
- 17 Oct 2026, 12:36 PM
- Fetched
- 18 Oct 2026, 3:31 AM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://llmspeed.dev/
- Source URL
- https://hnrss.org/best
Summary
- Score
- 7.0
- Created
- 18 Oct 2026, 3:31 AM
- Tags
- Audience
- developersvibe_codersai_ml_learnersstartup_founders
What happened
llmspeed.dev is a Show HN site that lets you browse local LLM inference speed by hardware (RTX 3090/4090/5090, Mac Studio M5 Ultra 256GB and 512GB, DGX Spark 128GB, Strix Halo 128GB, CPU-only Ryzen 9 9950X with 96GB DDR5, cloud H100/H200) or by model (Qwen 3.5/3.6 35B A3B, GPT-OSS 20B and 120B, Gemma 4 12B/31B, GLM-5.3, DeepSeek V4 Flash, Llama 3.1 8B, Nemotron 3.5 Lightning 30B A3B). The site states estimates are for a short prompt on a single device, that measured numbers come from community benchmarks (r/LocalLLaMA, localscore.ai), and that other figures are derived from similar setups or projected from specs; it is open source on GitHub. The Hacker News thread drew 211 points and 67 comments.
Why it matters
Before spending on a 24GB to 512GB GPU or unified-memory machine, you can compare projected tokens/sec for the specific model you plan to run — but treat any row marked as derived or spec-projected as an estimate, not a measurement, and check the source benchmark for the exact quantisation and context length, since 'short prompt on a single device' will not match your long-context agent workload. For builders in Malaysia weighing a local rig against cloud H100/H200 rental, the useful decision is whether the projected speed on your chosen card justifies the capital outlay versus per-hour cloud cost; the site gives you the comparison input, not the verdict.
Discussion angle
How much should a projected-from-specs number influence a hardware purchase? Pick two rows for a model you actually run and ask whether the measured-vs-derived gap is small enough to trust, and what the site would need to show (quantisation, context length, batch size) before you'd buy on its numbers.