Feel local LLM speed before you buy hardware
llmspeed lets you preview how fast a local LLM will actually run on your hardware before you spend money on it. You pick a device and a model, and the site simulates the tokens/sec and time-to-first-token you'd experience, based on verified community benchmarks.
Pick specific hardware (NVIDIA RTX 3090/4090/5090, AMD Radeon AI PRO R9700, Mac Studio M5 Ultra, NVIDIA DGX Spark, cloud H100/H200, and more) and see simulated tokens/sec and time-to-first-token for a given model.
Covers dozens of devices across categories: consumer NVIDIA GPUs (RTX 3060 through RTX 5090), AMD GPUs and Ryzen AI Max accelerators, Apple Silicon Macs (MacBook Pro, Mac mini, Mac Studio), data-center cards (Tesla V100, RTX PRO 6000, Instinct MI50), cloud GPUs (H100 80GB, H200 141GB), and even a CP
Look up speed estimates per model, including Qwen 3.5/3.6/3.8 variants, GLM-5.3 and GLM-5.3 Flash, Gemma 4, GPT-OSS 20B and 120B, DeepSeek V4/V4.1 Flash, Llama 3.1 8B, and Nemotron 3.5 Lightning 30B A3B.
Measured numbers come from community benchmarks (r/LocalLLaMA, localscore.ai and more); figures without direct measurements are derived from similar setups or projected from specs, and the site labels which is which.
The project is open source and available on GitHub.
Browse the hardware list by GPU, Mac, accelerator, or cloud instance, including VRAM/memory specs for each option.
Select the LLM you're considering running locally from the model list.
The tool estimates tokens/sec and time-to-first-token for a short prompt on a single device, so you can gauge whether performance will feel acceptable before buying hardware.
It simulates how fast a local LLM will feel on specific hardware — estimating tokens/sec and time-to-first-token — so you can judge performance before buying a GPU, Mac, or other device.
Measured figures come from community benchmarks (including r/LocalLLaMA and localscore.ai). Numbers without direct measurements are derived from similar setups or projected from hardware specs, and the site distinguishes between these.
Estimates are for a short prompt running on a single device. Multi-GPU setups and long-context workloads aren't part of the simulation.
A wide range, including NVIDIA consumer and data-center GPUs (RTX 3060–5090, Tesla V100, RTX PRO 6000, DGX Spark), AMD GPUs and Ryzen AI Max chips, Apple Silicon Macs (MacBook Pro, Mac mini, Mac Studio), Intel Arc Pro, and cloud H100/H200 instances, plus a CPU-only reference configuration.
Models listed include Qwen 3.5/3.6/3.8 variants, GLM-5.3 (and Flash), Gemma 4, GPT-OSS 20B and 120B, DeepSeek V4 and V4.1 Flash, Llama 3.1 8B, and Nemotron 3.5 Lightning 30B A3B.
Yes, the project is open source and hosted on GitHub.
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