Can I run this LLM? VRAM & speed calculator
Paste any Hugging Face model. Fitcheck reads its config live and shows the memory each quantization needs, how much context fits, and a tokens-per-second estimate on your GPU or Mac.
1 · Model
Model details (edit if a value is missing)
2 · Hardware
| Quantization | Weights | KV cache | Total | Fits? | Max context | Speed (tok/s) |
|---|
Estimates, not guarantees: real use differs by runtime (llama.cpp, Ollama, LM Studio, MLX, vLLM), batch size and drivers. Total includes ~1 GB runtime overhead. Speed is 50–80% of the memory-bandwidth limit for one user.
How Fitcheck calculates memory
Weights = parameters × bits per weight ÷ 8. Parameter counts come straight from the model's safetensors or GGUF metadata on Hugging Face, so new models work the day they are published. Bits per weight are llama.cpp's averages for each quant mix (Q4_K_M ≈ 4.85, Q8_0 = 8.5).
KV cache = 2 × layers × KV heads × head size × bytes × context tokens. Fitcheck reads layers, KV heads and head size from config.json, handles grouped-query attention, sliding-window layers (Gemma 3, gpt-oss) and DeepSeek's compressed MLA cache.
Mixture-of-experts models (Qwen3-30B-A3B, gpt-oss, DeepSeek) must fit all experts in memory, but each token only reads the active ones, so they generate much faster than a dense model of the same size.
How much of a Mac's memory can the GPU use?
By default macOS lets the GPU wire about two-thirds of unified memory up to 36 GB and about three-quarters above that. You can raise it (for example sudo sysctl iogpu.wired_limit_mb=57344 on a 64 GB Mac) at your own risk; tick the box to see what fits then.
What if it doesn't fit?
Try a smaller quantization (Q4_K_M is the usual sweet spot), a shorter context, or a quantized KV cache. On a PC, llama.cpp can keep some layers in system RAM ("offload"): it works, but generation drops to a few tokens per second.
Gated models
Meta Llama and Google Gemma configs need a login to download. Fitcheck then reads the same architecture from a public mirror or asks you to fill in the model details.
Is anything uploaded?
No. Your browser asks the public Hugging Face API for the model's metadata; your hardware choices stay on your device.