On a 16 GB GPU, 12 catalog models run fully on the GPU at an 8,192-token context. The most capable is gemma-4-26B-A4B-it at QAT-Q4_0 (needs ~15.8 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.
gemma-4-26B-A4B-it at QAT-Q4_0 · 26.5B params · needs ~15.8 GiB
One command to run it (llama.cpp):
llama-server -m gemma-4-26B_q4_0-it.gguf -c 8192 -ngl 999| Model | Sweet-spot quant | Fits in |
|---|---|---|
| gemma-4-26B-A4B-it google | QAT-Q4_0 Runs fully on GPU | ~15.8 GiB |
| Devstral-Small-2-24B-Instruct-2512 mistralai | IQ4_XS Runs fully on GPU | ~14.9 GiB |
| Mistral-Small-3.2-24B-Instruct-2506 mistralai | IQ4_XS Runs fully on GPU | ~14.9 GiB |
| gpt-oss-20b openai | F16 Runs fully on GPU | ~15 GiB |
| Kimi-VL-A3B-Instruct moonshotai | Q4_K_M Runs fully on GPU | ~11.6 GiB |
| DeepSeek-R1-Distill-Qwen-14B deepseek-ai | Q4_K_M Runs fully on GPU | ~11.2 GiB |
| Qwen3-14B Qwen | Q4_K_M Runs fully on GPU | ~11 GiB |
| Phi-4-reasoning microsoft | Q4_K_M Runs fully on GPU | ~11.4 GiB |
| phi-4 microsoft | Q4_K_M Runs fully on GPU | ~11.2 GiB |
| gemma-4-12B-it google | Q8_0 Runs fully on GPU | ~14.3 GiB |
| Qwen3-8B Qwen | Q8_0 Runs fully on GPU | ~10.6 GiB |
| DeepSeek-R1-Distill-Llama-8B deepseek-ai | Q8_0 Runs fully on GPU | ~10.3 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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