Tools

Estimate VRAM for any quantized LLM — or any model on Hugging Face — with a verdict for your card or mixed cards, the speed ceiling, the wait before the first token, how many people it can serve, and the memory to fine-tune it.

Generate runnable llama.cpp, Ollama, vLLM and ExLlamaV2 commands for any indexed model, including Docker and Compose variants.

Answer three questions about your hardware and priorities to get a quantization format, runtime and quant level that actually run together.

Put two quantized models side by side: parameters, context, VRAM at your chosen context length, speed and quality loss.

Stream one sentence at a card's measured or scaled token rate, side by side with another card. Bandwidth ceilings are labelled and are not played back as a speed.

One page per card — 78 of them — listing which of the 112 indexed models fit it comfortably at 4K context, at what quant level, with how much headroom.

4 head-to-head pages — GGUF vs AWQ, GGUF vs EXL2 and the rest — on hardware support, runtime and the models this index ships in both formats.