Can MacBook Pro 14/16 M5 Max 128GB run gpt-oss 120B?

Short answer

Yes. gpt-oss 120B Q8_0 fits fully on MacBook Pro 14/16 M5 Max 128GB at 16k context, using 64.0 of 89.1 GB usable, and generates about ~107 tokens/s (estimated, not measured).

Measured generation speed by quantization

No measured rows for gpt-oss 120B on MacBook Pro 14/16 M5 Max 128GB yet. The ~107 tok/s above is a bandwidth estimate (614 GB/s × 0.61 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 gpt-oss 120B

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

• Mac Studio (2025) M3 Ultra 96GB: Q8_0, 67 GB usable, ~92 tok/s estimated

Faster for gpt-oss 120B

• RTX PRO 6000 96GB: Q8_0, 95 GB usable, ~261 tok/s measured
• Mac Studio M5 Ultra 256GB: Q8_0, 179 GB usable, ~212 tok/s estimated
• Mac Studio M5 Ultra 512GB (ships late Oct 2026): Q8_0, 358 GB usable, ~212 tok/s estimated

Other models on MacBook Pro 14/16 M5 Max 128GB, similar size

• GLM-4.5-Air (hardware page): Q5_K_M, fits fully, ~33 tok/s (estimated)
• Nemotron 3.5 Super VL 120B-A12B (hardware page): Q4_K_M, fits fully, ~44 tok/s (estimated)
• Llama 3.3 70B (hardware page): Q4_K_M, fits fully, ~12 tok/s (scaled from measured)
• Qwen3.6 35B-A3B (hardware page): Q8_0, fits fully, ~71 tok/s (measured)

Memory and context limits

Q8_0 weights are 63.4 GB; the 16k f16 KV cache adds 0.61 GB. Largest context that fits in 89.1 GB usable:

Native limit 128k. Full KV cache table for gpt-oss 120B.

Copy-paste commands

The advisor's pick for this hardware: Q8_0, 16k context.

llama-server -hf unsloth/gpt-oss-120b-GGUF:Q8_0 -c 16384 -fa on --jinja -ngl 99

OLLAMA_CONTEXT_LENGTH=16384 ollama serve
ollama run hf.co/unsloth/gpt-oss-120b-GGUF:Q8_0

pip install -U mlx-lm
mlx_lm.generate --model mlx-community/gpt-oss-120b-MXFP4-Q8 --prompt "Hello" --max-tokens 256
mlx_lm.server --model mlx-community/gpt-oss-120b-MXFP4-Q8 --port 8080

MLX runs on Apple Silicon only. Repo: mlx-community/gpt-oss-120b-MXFP4-Q8 (converted with mlx-lm, 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 Max 128GB · gpt-oss 120B on all hardware · VRAM tier · all measured pages · leaderboard

Compare: MacBook Pro 14/16 M5 Max 128GB vs MacBook Pro 14/16 (2024) M4 Max 128GB