On a 32 GB GPU, 2 catalog models run fully on the GPU at an 8,192-token context. The most capable is Qwen3-Coder-30B-A3B-Instruct at Q4_K_M (needs ~20.3 GiB). Pick a smaller model or a lower quant for more headroom.
Figures assume a 32 GB GPU paired with 32 GB of system RAM (a typical desktop), judged at an 8,192-token context. Speed depends on the specific card — this page ranks by fit, not speed.
Qwen3-Coder-30B-A3B-Instruct at Q4_K_M · 30.5B params · needs ~20.3 GiB
One command to run it (llama.cpp):
llama-server -m Qwen3-Coder-30B-A3B-Instruct-Q4_K_M.gguf -c 8192 -ngl 999| Model | Sweet-spot quant | Fits in |
|---|---|---|
| Qwen3-Coder-30B-A3B-Instruct Qwen | Q4_K_M Runs fully on GPU | ~20.3 GiB |
| Devstral-Small-2-24B-Instruct-2512 mistralai | Q8_0 Runs fully on GPU | ~27.5 GiB |
Derived live from the fit engine + catalog at an 8,192-token context. "Fits in" is the modelled VRAM the sweet-spot quant needs (weights + KV cache + overhead). Speed depends on your specific card — check a GPU page or the calculator.
selected to compare · pick at least 2