nicorobinreal · 2026-10-17 · notable
llmspeed — feel how fast a local LLM runs before you buy hardware
llmspeed is an open-source web app that streams text at the estimated tokens per second of a chosen GPU, Mac or CPU running a local LLM. It covers 30 devices and 15 models, with a race mode and a memory-fit check. MIT.
Pick a GPU or Mac and a model, then watch the answer stream at the speed that setup would reach.
What is it?
llmspeed simulates local-LLM speed in the browser: it waits the estimated time to first token, then streams text at the predicted tokens per second for the chosen hardware, model and quantization. A race mode runs two setups side by side, such as an RTX 5090 against a Mac Studio. It covers 30 GPUs, Macs and CPUs and 15 popular models across CUDA, Metal, ROCm, Vulkan and CPU.
How does it work?
Every estimate is labelled Measured (a benchmark on that exact setup from r/LocalLLaMA, localscore.ai or contributor PRs), Derived (interpolated from similar setups) or Projected (from specs). Without a benchmark, llmspeed models generation as memory-bandwidth bound, counting only active parameters for MoE models, and prompt processing as compute bound. A memory check counts weights, KV cache and overhead to say whether a quant fits, needs partial offload or will not run.
Why does it matter?
A tokens-per-second figure is hard to judge until you see text arrive at that pace, and local-LLM hardware is expensive. Seeing the speed first helps buyers choose between a discrete GPU and a Mac. The README notes estimates can be 10–30% off, and Hacker News commenters reported numbers that did not match their own machines, so measured submissions matter.
Who is it for?
people sizing a local-LLM machine
Try it
https://llmspeed.dev