Can Apple M3 Ultra run Qwen3.8 27B?

Q: Can Apple M3 Ultra run Qwen3.8 27B?

Yes. Measured 12.46 tok/s generation on Apple M3 Ultra, via MLX (mlx_lm.benchmark), BF16, 8,192-token context. Source: https://machinelearning.apple.com/research/mlx-distributed-inference

Yes. Measured 12.46 tok/s generation (MLX (mlx_lm.benchmark), BF16, 8,192-token context; source).

Every measured result for Qwen3.8 27B on Apple M3 Ultra

Build	Quant / setup	Engine	tok/s
Mac Studio M3 Ultra 512GB: Qwen3.8 27B and Mistral Medium 3.5 (Apple MLX benchmark)	BF16, 8,192-token context	MLX (mlx_lm.benchmark)	12.46	source

Memory: Apple M3 Ultra has 512 GB. Qwen3.8 27B weights are about 16.5 GB at Q4_K_M (27B params × 4.89 bits ÷ 8, before context/KV cache). Arithmetic, not a measurement; long contexts need more. Check other quants in the VRAM calculator.

Speeds are decode (generation) tokens/sec as reported by each source; quant, engine and context differ, so small gaps are noise.

All measured speeds for Qwen3.8 27B: best home rig for Qwen3.8 27B.

Qwen3.8 27B on other hardware: 2x Apple M5 Ultra · 2x Intel Arc Pro B70 · 2x RTX 5070 Ti · 2x Tesla V100 32GB · 4x Apple M5 Ultra · Apple M5 Ultra · DGX Spark (GB10) · Intel Arc Pro B70 · RTX 3060 12GB · RTX 3090 · RTX 4060 Ti 16GB · RTX 5070 Ti · RTX 5090 · RTX Pro 6000 Max-Q (Blackwell, 96 GB, 300 W) · RTX Spark N1X · Radeon AI Pro R9700 · Strix Halo (Radeon 8060S)

Other models measured on Apple M3 Ultra: DeepSeek R1 671B · Mistral Medium 3.5 128B · Muse Spark Open 816B-A42B · gpt-oss 20B

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