Can MacBook Pro 14/16 M5 Pro 48GB run Qwen3.6 27B?

Short answer

Yes. Qwen3.6 27B Q4_K_M fits fully on MacBook Pro 14/16 M5 Pro 48GB at 16k context, using 18.1 of 33.1 GB usable, and generates about ~13 tokens/s (estimated, not measured).

Measured generation speed by quantization

No measured rows for Qwen3.6 27B on MacBook Pro 14/16 M5 Pro 48GB yet. The ~13 tok/s above is a bandwidth estimate (307 GB/s × 0.69 efficiency). Have this machine? Submit a llama-bench result and this page will switch to measured data.

Generation = tg128 (128 generated tokens, short context). Public rows are medians of community llama.cpp results (sources); our own runs use llama-bench on a rented GPU.

Alternatives

Less memory that still fits Qwen3.6 27B

Smaller memory usually means a cheaper card or Mac (we do not list prices).

• Mac mini M6 32GB: Q4_K_M, 22 GB usable, ~6 tok/s estimated
• MacBook Air M5 32GB: Q4_K_M, 22 GB usable, ~6 tok/s estimated
• RTX 4090 24GB: Q5_K_M, 23 GB usable, ~42 tok/s scaled from measured

Faster for Qwen3.6 27B

• RTX 3090 24GB: Q5_K_M, 23 GB usable, ~36 tok/s scaled from measured
• AMD RX 7900 XTX 24GB: Q5_K_M, 23 GB usable, ~30 tok/s scaled from measured
• RTX 5090 32GB: Q8_0, 31 GB usable, ~52 tok/s measured

Other models on MacBook Pro 14/16 M5 Pro 48GB, similar size

• Qwen3.8 27B (hardware page): UD-Q4_K_M, fits fully, ~13 tok/s (estimated)
• Gemma 4 26B-A4B (hardware page): Q8_0, fits fully, ~43 tok/s (estimated)
• Qwen3-Coder 30B-A3B (hardware page): Q6_K, fits fully, ~62 tok/s (estimated)
• GLM-4.7-Flash (hardware page): Q8_0, fits fully, ~55 tok/s (estimated)

Memory and context limits

Q4_K_M weights are 16.8 GB; the 16k f16 KV cache adds 1.23 GB. Largest context that fits in 33.1 GB usable:

Native limit 256k, 1.01M with YaRN. Full KV cache table for Qwen3.6 27B.

Copy-paste commands

The advisor's pick for this hardware: Q4_K_M, 16k context.

llama-server -hf unsloth/Qwen3.6-27B-GGUF:Q4_K_M -c 16384 -fa on --jinja -ngl 99

OLLAMA_CONTEXT_LENGTH=16384 ollama serve
ollama run hf.co/unsloth/Qwen3.6-27B-GGUF:Q4_K_M

pip install -U mlx-vlm
mlx_vlm.generate --model mlx-community/Qwen3.6-27B-4bit --prompt "Hello" --max-tokens 256
mlx_vlm.server --model mlx-community/Qwen3.6-27B-4bit

MLX runs on Apple Silicon only. Repo: mlx-community/Qwen3.6-27B-4bit (converted with mlx-vlm, per its model card). These commands use MLX's default context handling; there is no MLX speed on this site yet.

MLX on this Mac

MLX is Apple's machine-learning framework for Apple Silicon. Where an mlx-community conversion exists, the commands above include an MLX block next to llama.cpp and ollama.

• File size. MLX's default quantization is affine with a group size of 64, and every group stores a scale and a bias (MLX docs). With 16-bit scales and biases that works out to about 4.5 bits per weight at 4-bit and 8.5 at 8-bit. llama.cpp lists Q4_K_M at about 4.89 and Q8_0 at about 8.50 bits per weight (llama.cpp quantize README). So an MLX 4-bit repo is usually a little smaller than Q4_K_M and MLX 8-bit is about the same size as Q8_0. Real repos differ because some layers (embeddings, vision towers, MoE experts in MXFP4 for gpt-oss) are stored differently, so we show each repo's actual size.
• Quality. They are different schemes. MLX 4-bit applies one bit width with per-group scale and bias, while Q4_K_M is a K-quant mix that keeps some tensors at higher precision (llama.cpp k-quants PR). We have not found a rigorous like-for-like quality comparison, so we only pair them by bit class: MLX 4-bit with Q4-class GGUF and MLX 8-bit with Q8_0.
• Speed. MLX speeds are not shown on this site yet. Published comparisons often find MLX faster than llama.cpp on Macs: an M2 Ultra study measured about 230 tok/s for MLX against about 150 for llama.cpp (short context) on Qwen-2.5 models (arXiv 2511.05502), and an M4 Max table shows mlx-lm ahead of llama.cpp on most 4-bit text models but behind on Gemma 3 4B (arXiv 2601.19139). So MLX may well run faster than the llama.cpp numbers shown here, but not for every model, and we do not estimate by how much.
• Which package. Text-only conversions run with mlx-lm. Many recent mlx-community repos (Gemma 4, Qwen3.5/3.6/3.8, Qwen3-VL) were converted with mlx-vlm and their model cards say to run them with it, so the command we show follows each repo's card.

Repos are checked against the Hugging Face API (how).

FAQ

Related

All models on MacBook Pro 14/16 M5 Pro 48GB · Qwen3.6 27B on all hardware · VRAM tier · all measured pages · leaderboard