Llmspeed plays a local LLM at its real speed before you buy

Picking the Mac or GPU for a local AI model usually comes down to a Reddit thread and a bar chart with no feel for what the number means in practice. Llmspeed, posted to Hacker News on 17 October 2026, hands you the running stream instead: pick a model and a device, watch the tokens arrive, and decide if the pace is one you can read along to.

The site covers 30 GPUs, Macs and CPUs and 15 open models at their common quantisations. Hardware ranges from a consumer RTX 3060 to a rented H200 141GB cloud box and the Apple silicon Macs that have become the default local-model buy. The models include Qwen, DeepSeek, Llama and Gemma variants in several sizes. Behind the demo the project is 40 stars on GitHub and MIT licensed. The Show HN thread had 91 points at the time of writing.

The three honesty tiers

Llmspeed's author, who posts as nicorobinreal, splits every number into one of three bands and labels it on the card. Measured means the pair of hardware and model were actually benchmarked at that quantisation. Derived means the figure is interpolated from benchmarks of a close setup. Projected means it is a calculation from the hardware specs, used for machines a buyer cannot try yet. The README says real numbers will land within 10 to 30 percent of the derived or projected figures. Published community benchmarks from r/LocalLLaMA, localscore.ai and llama.cpp issue threads are the input.

What the simulation is doing

Under the stream, Llmspeed assumes a token reads the active model weights once from memory, which is the dominant cost for a modern transformer on consumer hardware. It applies a per-backend efficiency coefficient fit against the measured benchmarks, and the result is a tokens-per-second number for a short prompt on a single device. The display then plays that stream back at the right rate so the pace is readable rather than a number.

The limits the project states out loud

Three. First, single device only: no multi-GPU setups and no distributed inference, which is where some of the biggest local setups actually live. Second, the estimates assume a short prompt, so the number drifts for the long contexts an agent would feed in. Third, projected figures for unreleased hardware sit between an educated guess and a specification sheet until a real card is in a real benchmark.

For a working developer picking up a local setup for a side project or a client demo, the practical question is which of their options can run the model they want at a speed that will not break the flow. Reading one card at a time against the stream makes that call quicker than a spreadsheet of tokens per second. For a team ordering machines, it is worth running the chosen model on an actual loan unit before signing a purchase order, because 20 percent off the simulated rate is the gap between a usable rig and one that sits in a drawer.

Source

Llmspeed, posted on Hacker News by nicorobinreal. Code on GitHub, MIT licence.