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Llmspeed estimates how fast local LLMs run on 30 devices

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Llmspeed is a website, posted to Show HN, built around one question: how fast will your local model feel? Its tagline is "feel local LLM speed before you buy". The idea is to let someone weigh up hardware before spending money, rather than discovering the speed afterwards.

The site is organised two ways. "Browse by hardware" lists 30 entries. Most are consumer and workstation parts: NVIDIA cards from the RTX 3060 12GB, 5060 Ti 16GB and 5070 Ti 16GB up through the RTX 3090, 4090, 5080 and 5090 32GB, plus the RTX PRO 6000 96GB and the older Tesla V100 32GB. AMD is represented by the RX 7900 XT and XTX, the RX 9070 XT, the Radeon AI PRO R9700, the Instinct MI50, and the Ryzen AI Max+ 395 128GB (Strix Halo) and Ryzen AI Max+ PRO 495 192GB (Gorgon Halo). Intel appears with the Arc Pro B70 32GB. Apple entries include the Mac Studio M5 Ultra in 256GB and 512GB, the Mac Studio M3 Ultra 512GB and M4 Max 128GB, the Mac mini M5 Pro 64GB, and MacBooks with M5 Max, M5 Pro and M4 Pro chips. Also listed are the NVIDIA DGX Spark 128GB, an NVIDIA RTX Spark N1X 128GB laptop, one CPU-only setup (Ryzen 9 9950X with 96GB DDR5), and two cloud GPUs, the H100 80GB and H200 141GB.

"Browse by model" lists 15 models: Qwen 3.6/3.8 27B, Qwen 3.8 Flash-Next, Qwen 3.5/3.6 35B A3B, Qwen 3.5 9B, GLM-5.3 and GLM-5.3 Flash, Gemma 4 in 12B, 26B A4B and 31B sizes, GPT-OSS 20B and 120B, DeepSeek V4 Flash and V4.1 Flash, Llama 3.1 8B, and Nemotron 3.5 Lightning 30B A3B.

The footnote sets the terms. The figures are "estimates for a short prompt on a single device". Measured numbers come from community benchmarks, including r/LocalLLaMA and localscore.ai; other numbers are derived from similar setups or projected from specs. The project is open source on GitHub.

The page text carries no speed figures of its own, so the actual numbers sit behind the hardware and model pages.

Key facts

  • Llmspeed (llmspeed.dev) is a Show HN site for judging how fast a local LLM will feel before you buy hardware.
  • It can be browsed by hardware (30 entries, from an RTX 3060 12GB to Mac Studio M5 Ultra 512GB and cloud H200 141GB) and by model (15 entries).
  • The figures are estimates for a short prompt on a single device.
  • Measured numbers come from community benchmarks such as r/LocalLLaMA and localscore.ai; others are derived from similar setups or projected from specs.
  • The project is open source on GitHub.

Why it matters

Buying hardware for local models is expensive, and memory size, chip family and model choice all change the outcome. Llmspeed puts a wide range of devices and current open models in one place so a buyer can compare before paying. The list spans cheap cards like the RTX 3060 12GB, large unified-memory machines like the Mac Studio M5 Ultra 512GB, and cloud GPUs like the H100 and H200 as reference points.

Who it affects

Anyone weighing a purchase for running models locally: people choosing between an NVIDIA GPU, an AMD card or APU, a Mac, or a CPU-only box. The model list (Qwen, Gemma, GLM, GPT-OSS, DeepSeek, Llama, Nemotron) also suits those who already know which model they want and need to find hardware that suits it.

How to use it

Open llmspeed.dev and start from either side: pick a device under "Browse by hardware" or a model under "Browse by model". Read the result as a guide to how the model will feel on that machine, for a short prompt on a single device. The code is open source on GitHub.

How solid is it

Treat the numbers as estimates, which is what the site itself says. Some are measured, taken from community benchmarks such as r/LocalLLaMA and localscore.ai. Others are derived from similar setups or projected from specs. The page does not say how many fall into each group.

Risks and caveats

The scope is narrow: a short prompt on a single device. Results for longer prompts or multi-device setups are outside what the site claims to cover. Projected figures can be off from real performance, and the page gives no details on quantisation or inference software. Check a projected entry against community benchmarks before committing money to it.

“Estimates for a short prompt on a single device.”

— llmspeed.dev