On a 48 GB Mac, 12 catalog models run fully on the GPU at an 8,192-token context. The most capable is Hunyuan-A13B-Instruct at IQ4_XS (needs ~46.2 GiB). Pick a smaller model or a lower quant for more headroom.
Figures assume a 48 GB unified-memory Mac (one pool shared by CPU and GPU), judged at an 8,192-token context. Speeds depend on the specific chip — see the GPU pages.
Hunyuan-A13B-Instruct at IQ4_XS · 80.4B params · needs ~46.2 GiB
One command to run it (llama.cpp):
llama-server -m tencent_Hunyuan-A13B-Instruct-IQ4_XS.gguf -c 8192 -ngl 999| Model | Sweet-spot quant | Fits in |
|---|---|---|
| Hunyuan-A13B-Instruct tencent | IQ4_XS Runs fully on GPU | ~46.2 GiB |
| Kimi-Dev-72B moonshotai | IQ4_XS Runs fully on GPU | ~43.8 GiB |
| Kimi-Linear-48B-A3B-Instruct moonshotai | Q4_K_M Runs fully on GPU | ~33.3 GiB |
| Qwen3.6-35B-A3B Qwen | Q8_0 Runs fully on GPU | ~39 GiB |
| Qwen3-Omni-30B-A3B-Instruct Qwen | Q4_K_M Runs fully on GPU | ~19.6 GiB |
| Qwen2.5-VL-32B-Instruct Qwen | Q8_0 Runs fully on GPU | ~38.3 GiB |
| DeepSeek-R1-Distill-Qwen-32B deepseek-ai | Q8_0 Runs fully on GPU | ~38.3 GiB |
| Qwen3-32B Qwen | Q8_0 Runs fully on GPU | ~38.3 GiB |
| gemma-4-31B-it google | Q8_0 Runs fully on GPU | ~34 GiB |
| GLM-4.7-Flash zai-org | Q8_0 Runs fully on GPU | ~36.1 GiB |
| Qwen3-Coder-30B-A3B-Instruct Qwen | Q8_0 Runs fully on GPU | ~34.6 GiB |
| Qwen3-30B-A3B-Instruct-2507 Qwen | Q8_0 Runs fully on GPU | ~34.6 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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