On a 6 GB GPU, 11 catalog models run fully on the GPU at an 8,192-token context. The most capable is gemma-4-E4B-it at QAT-Q4_0 (needs ~5.8 GiB). Pick a smaller model or a lower quant for more headroom.
Figures assume a 6 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-E4B-it at QAT-Q4_0 · 8B params · needs ~5.8 GiB
One command to run it (llama.cpp):
llama-server -m gemma-4-E4B_q4_0-it.gguf -c 8192 -ngl 999| Model | Sweet-spot quant | Fits in |
|---|---|---|
| gemma-4-E4B-it google | QAT-Q4_0 Runs fully on GPU | ~5.8 GiB |
| DeepSeek-R1-Distill-Qwen-7B deepseek-ai | Q4_K_M Runs fully on GPU | ~5.7 GiB |
| gemma-4-E2B-it google | Q8_0 Runs fully on GPU | ~5.7 GiB |
| Qwen3-Embedding-4B Qwen | Q4_K_M Runs fully on GPU | ~4.2 GiB |
| Phi-4-mini-instruct microsoft | Q8_0 Runs fully on GPU | ~5.7 GiB |
| SmolLM3-3B HuggingFaceTB | Q8_0 Runs fully on GPU | ~4.4 GiB |
| Qwen3-ASR-1.7B Qwen | Q8_0 Runs fully on GPU | ~2.7 GiB |
| DeepSeek-R1-Distill-Qwen-1.5B deepseek-ai | Q8_0 Runs fully on GPU | ~2.7 GiB |
| Qwen3-ASR-0.6B Qwen | Q8_0 Runs fully on GPU | ~1.3 GiB |
| Qwen3-Reranker-0.6B Qwen | BF16 Runs fully on GPU | ~2.6 GiB |
| Qwen3-Embedding-0.6B Qwen | Q8_0 Runs fully on GPU | ~2 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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