Last updated: October 17, 2026

Llmspeed: Feel How Fast a Local LLM Runs Before You Buy

Llmspeed is a free, open-source web tool at llmspeed.dev. You pick a GPU, Mac or CPU and a model, and it streams a sample answer at the speed that hardware is predicted to reach, including the wait before the first token. It appeared on Hacker News as a Show HN on October 17, 2026.

What Is Llmspeed?

Most local LLM benchmarks give you a tokens-per-second figure. Llmspeed turns that figure into something you can watch: the text appears at the predicted rate, after a realistic delay for your prompt length. The project describes itself as a way to “feel how fast a local LLM runs before you buy the hardware.”

It is a static website built with Vite, Preact and TypeScript, released under the MIT license. All predictions live in one data file in the GitHub repository, so anyone can check where a number came from.

• Real-time simulation. Text streams at the predicted tokens per second, after a time to first token that depends on prompt length.
• Race mode. Runs two setups side by side, for example an RTX 5090 against a Mac Studio.
• Memory fit. Shows whether a model fits at each quantization, needs partial offload to system RAM, or will not run at all.
• Coverage. 30 GPUs, Macs and CPUs and 15 popular models, across CUDA, Metal, ROCm, Vulkan and CPU backends.
• Shareable links. Every setup or race has its own link you can send to someone.

Source: GitHub: albinstman/llmspeed (README)

Source: llmspeed.dev (official site)

How to Use Llmspeed

1. Open llmspeed.dev in a browser. Nothing to install and no account; the page needs JavaScript.
2. Pick the hardware you own or are thinking of buying, such as an RTX 4090, a Mac Studio or a CPU-only PC.
3. Pick a model and a quantization (for example Q4_K_M, Q8_0 or FP16). The memory-fit indicator tells you if it fits, spills into system RAM or will not load.
4. Choose a prompt length. Longer prompts take longer before the first token appears; the data file has presets of 42, 290, 1,800 and 12,500 tokens.
5. Start the simulation and watch the answer stream. Check the label next to the result: Measured, Derived or Projected.
6. Turn on race mode to put a second setup beside the first, then copy the link to share the comparison.

Llmspeed simulates speed only. It does not run a model in your browser or tell you anything about answer quality.

Source: GitHub: albinstman/llmspeed (README)

Source: llmspeed.dev (official site)

Source: GitHub: llmspeed data/app-data.json

How Accurate Is Llmspeed? Measured, Derived and Projected

Every estimate carries a label that says how much to trust it:

In the data file we read on October 17, 2026, 76 of 1,446 hardware, model and quantization combinations were Measured, 385 were Derived and 985 were marked as not able to run. So most runnable results are interpolated, not measured directly.

Source: GitHub: albinstman/llmspeed (README)

Source: GitHub: llmspeed data/app-data.json

How Llmspeed Estimates Tokens per Second

Measured numbers come from community benchmarks collected and verified from r/LocalLLaMA, localscore.ai, llama.cpp discussions and other sources. When no benchmark exists, the README says the tool falls back to first principles:

• Generation speed is bound by memory bandwidth: every token reads the active weights once. For mixture-of-experts models, only the active parameters count.
• Prompt processing (time to first token) is bound by compute, measured as FP16 TFLOPS.
• Both use per-backend efficiency factors calibrated against the measured benchmarks, plus a slowdown as the context grows.
• Partial offload uses a hybrid model: the share on the GPU runs at GPU bandwidth and the share spilled to system RAM at an effective RAM bandwidth, calibrated from measured offload runs.
• Memory fit counts weights, KV cache and runtime overhead against usable memory: about 75% of unified memory on Macs and 93% of VRAM on discrete GPUs.

Our Qwen4 VRAM calculator · How to run Qwen4 locally

Source: GitHub: albinstman/llmspeed (README)

Source: r/LocalLLaMA

Source: localscore.ai

Source: GitHub: llama.cpp

Llmspeed Hardware List: 30 GPUs, Macs and CPUs

These are the devices in the project’s data file on October 17, 2026, sorted by memory bandwidth, which is the main driver of generation speed in its model. Memory and bandwidth are the figures the file uses.

Source: GitHub: llmspeed data/app-data.json

Which Models Does Llmspeed Cover?

The data file lists 15 models, chosen by popularity: Qwen 3.6/3.8 27B, Qwen 3.5/3.6 35B A3B, Qwen3 30B A3B, Qwen 3.5 9B, GLM-5.3 Flash, DeepSeek V4 Flash, DeepSeek R1 Distill 14B, GPT-OSS 20B, GPT-OSS 120B, Gemma 4 26B A4B, Gemma 4 31B, Llama 3.1 8B, Ornith 1.5 35B A3B, Ornith 1.5 9B, Muse Glimmer 30B.

Mixture-of-experts models such as GPT-OSS 120B, DeepSeek V4 Flash and GLM-5.3 Flash only read their active parameters per token, which is why they can generate faster than their total size suggests, as long as they fit in memory. The largest ones are offered in low-bit quantizations so they can fit on high-memory Macs and workstations.

Source: GitHub: llmspeed data/app-data.json

Source: GitHub: albinstman/llmspeed (README)

Add Your Own Benchmark

The most valuable contribution is a real measurement. Run llama.cpp’s benchmark on your machine, append a row to benchmarks/submissions.csv (hardware, model, quant, backend, engine version, gen_tps, prefill_tps, prompt tokens, source link, notes) and paste the full llama-bench output in the pull request. Results from MLX, vLLM or ExLlama are welcome if you name the engine.

# 1. Measure your own machine with llama.cpp's benchmark tool
llama-bench -m your-model.gguf -p 512 -n 128
# tg128 = generation speed (gen_tps), pp512 = prompt speed (prefill_tps)
# 2. Add a row to benchmarks/submissions.csv and open a pull request

Source: GitHub: albinstman/llmspeed (README)

Source: GitHub: llmspeed benchmarks/submissions.csv

Source: GitHub: llama.cpp

Run Llmspeed Locally

The site is fully static, so you can run your own copy. The README’s development commands:

# Run the site locally (Vite + Preact + TypeScript)
git clone https://github.com/albinstman/llmspeed
cd llmspeed
npm install
npm run dev

Source: GitHub: albinstman/llmspeed (README)

Llmspeed Limitations

• Single device only: multi-GPU setups (tensor or layer split) are not simulated yet.
• Estimates, not guarantees: drivers, engine versions, flash attention, power limits and thermals can move real numbers by 10–30%, according to the README.
• Projected numbers for unreleased hardware will be replaced with measurements as they appear.
• On Hacker News, some commenters said the predictions did not match their own machines, asked for multi-GPU, NVLink and more quantization options, and pointed out that prompt processing and context length often matter more than raw generation speed for real work.
• One suggestion from the same discussion: renting a cloud GPU for a few minutes is a cheap way to test a specific setup for real before buying.

Source: GitHub: albinstman/llmspeed (README)

Source: daily.dev: summary of the Show HN discussion (Oct 17, 2026)

Independent guide, not affiliated with Llmspeed or its author. Facts are from the Llmspeed README and data file on GitHub and a summary of the Hacker News discussion, read on October 17, 2026. We have not run our own benchmarks. Hardware and model names are trademarks of their owners.