On a 16 GB GPU, 1 catalog model run fully on the GPU at an 8,192-token context. The most capable is Devstral-Small-2-24B-Instruct-2512 at IQ4_XS (needs ~14.9 GiB). Pick a smaller model or a lower quant for more headroom.
Figures assume a 16 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.
Devstral-Small-2-24B-Instruct-2512 at IQ4_XS · 24B params · needs ~14.9 GiB
One command to run it (llama.cpp):
llama-server -m Devstral-Small-2-24B-Instruct-2512-IQ4_XS.gguf -c 8192 -ngl 999| Model | Sweet-spot quant | Fits in |
|---|---|---|
| Devstral-Small-2-24B-Instruct-2512 mistralai | IQ4_XS Runs fully on GPU | ~14.9 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.
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