LLMSpeed: Preview Local LLM Performance Before Buying
Original: Show HN: Llmspeed – feel how fast a local LLM runs before you buy the hardware
Why This Matters
Bridges the spec-sheet gap for the fast-growing local AI hardware market.
LLMSpeed.dev lets users preview token generation speeds for local LLMs across 30+ hardware configs—from RTX 3060 to M5 Ultra—before purchasing, using community benchmarks from r/LocalLLaMA and localscore.ai.
LLMSpeed is an open-source tool that simulates how fast a local large language model will feel on specific hardware before you commit to buying it. The site covers over 30 GPU and CPU configurations—ranging from budget options like the NVIDIA RTX 3060 12GB to high-end setups like the Mac Studio M5 Ultra 512GB and NVIDIA RTX 5090 32GB—alongside cloud references like H100 and H200. Models span Llama 3.1 8B, Qwen 3.x series, Gemma 4, DeepSeek V4/V4.1, and others. Speed estimates are based on short-prompt, single-device scenarios. Data comes from community benchmarks (r/LocalLLaMA, localscore.ai), with gaps filled by projections from similar hardware specs. The project is fully open-source on GitHub under the handle nicorobinreal/llmspeed, making the underlying data and methodology transparent and forkable. It targets hobbyists and developers who want a practical feel for inference speed—tokens per second—rather than abstract FLOPS numbers, before spending thousands on hardware.