What the local AI model advisor does
Wondering “can my PC run this LLM?” This tool answers it. It auto-detects your GPU through the browser's WebGPU API, takes your memory, and shows which popular local models, Llama 3, Mistral, Qwen, Gemma and more, your machine can run, at which quantization, and roughly how fast. Everything runs in your browser; nothing is uploaded.
Why check before you download
Local model files are several gigabytes each, and a model that's too big for your memory either won't load or crawls. Checking first saves long downloads and frustration, and shows you the largest model you can run smoothly.
How to use it
- Let it auto-detect, or set your GPU memory (VRAM) and system RAM with the sliders. Apple Silicon: use your total unified memory as the GPU memory.
- Pick a quantization (Q4_K_M is the recommended sweet spot).
- Read the table: each model shows its memory need and whether it runs on your GPU, partially offloads, runs on CPU only, or won't fit.
Understanding the results
- Runs on GPU — fits in video memory, smooth and fast.
- Partial offload — some layers on GPU, some on CPU; usable but slower.
- CPU only — fits in RAM but not VRAM; works, but slow.
- Won't fit — too big for your memory at this quantization; try a smaller model or lower quant.
Quantization in brief
Quantization stores model weights at lower precision to save memory. Q4_K_M is about a quarter the size of full FP16 with only a small quality drop, which is why it's the default for most people. Step up to Q5, Q6 or Q8 if you have memory to spare and want a little more quality.
Good to know
- WebGPU can't read exact VRAM, so verify the auto-detected value.
- Memory estimates add ~1.6 GB overhead; long context windows need more.
- Run models with LM Studio, Ollama or Jan once you know what fits.