Llmspeed's Local LLM Speed Simulator Has 76 Measured Figures Behind 461 Runnable Combinations

Llmspeed, a free site that simulates how fast a local language model would run on a chosen GPU or Mac, reached 144 points on Hacker News on October 17. Of the 461 runnable hardware-and-model combinations in its data file, 76 are measured. The rest are estimates.

What llmspeed shows

The tool lives at llmspeed.dev and in the nicorobinreal/llmspeed repository, which is MIT-licensed, was created on August 28 and had 47 stars at last count. You pick a device and a model, then watch a dummy answer stream at the predicted speed, including the wait for the first token. It covers 30 devices and 15 models. The page describes its numbers as "Estimates for a short prompt on a single device".

The README sorts every figure into three tiers: Measured, meaning benchmarked on the exact setup; Derived, meaning interpolated from similar setups; and Projected, meaning calculated from specs. Where no benchmark exists, it limits generation speed by memory bandwidth, counting only active parameters for mixture-of-experts models, and limits prompt processing by compute. The README warns that real results can vary 10 to 30 percent with drivers and engine versions.

How much of it is measured

The repository's data file, stamped August 29, holds 1,446 combinations of device, model and quantization. In it, 985 are marked as will not run, 385 as derived and 76 as measured.

1. deepseek-r1-14b22 measured entries
2. llama-3.1-8b19 measured entries
3. qwen3.6-35b-a3b9 measured entries
4. gpt-oss-20b9 measured entries
5. gemma-4-26b-a4b8 measured entries
6. gemma-4-31b3 measured entries
7. gpt-oss-120b2 measured entries
8. deepseek-v4-flash2 measured entries
9. qwen3.6-27b1 measured entries
10. glm-5.3-flash1 measured entries

Source: llmspeed data/app-data.json on GitHub main branch, read 2026-10-17

Two older models, Llama 3.1 8B and DeepSeek R1 Distill 14B, account for 41 of the 76 measured entries. Qwen 3.6 27B, the model the data file ranks first in popularity, has one. The page lists Mac Studio M5 Ultra 512GB as announced rather than released, and its data has no measured entry. The RTX 3060 has none either.

The sources it names are the r/LocalLLaMA forum and LocalScore, a Mozilla Builders project. LocalScore's front page listed 43 results across three models: Llama 3.2 1B, Llama 3.1 8B and Qwen2.5 14B. That is a thin base for a 27B-class estimate.

What Hacker News users say

The Show HN thread had 49 comments. A commenter named pil0u said an M2 Ultra with 128GB runs almost twice as fast as the figure listed for the M3 Ultra with 512GB, and that they did not know what the numbers were based on. A user named adrian17 said Gemma 26B-A4B on an RTX 5060 Ti looked optimistic, reporting about 40 tokens a second at Q4. Another, tom_alexander, said the site claims a 9070 XT cannot run Qwen 27B at FP16 even though llama.cpp can offload layers to system memory.

A commenter named numpad0 called it "just a tok/s simulator", which undercuts the word "feel" in the pitch. A commenter named albinstman replied that "I'll look into adding harnesses" and that benchmark sources "usually don't contain harness" data. The thread did not establish who albinstman is relative to the repository owner.

Related coverage on this site includes PS5LM, which runs a 27B Qwen model on a jailbroken PS5 at about 20 tokens a second, a measured figure on one device, and VoiceStudio, a local ElevenLabs rival.

Where to check before buying

The README invites contributions through a pull request to benchmarks/submissions.csv. When the file was read on October 17 it held only its header row, so no community benchmark has been merged through that route. A buyer sizing a card for a 27B model should treat the number as a derived estimate and check it against a thread for the exact card.