Can MacBook Pro M5 Max 128 GB run Qwen3-Next 80B-A3B Instruct?

128 GB unified · 614 GB/s memory bandwidth · Qwen3-Next 80B-A3B Instruct has 81.3B parameters (3B active per token).

Short answer

Yes, excellent for chat and comfortable or better for every workload we test.

Best quantization for chat: 6-bit (62 GB of weights) on LM Studio. Generation: 90–150 tok/s, leaving about 55 GB free for your other apps.

How it rates for each workload

Same model and machine, different jobs. Agents and long documents send far bigger prompts than chat, so they need much more speed and memory.

Workload	Verdict	Quant	Generation	First reply	Context
Chatwith Open WebUI	Excellent	6-bit	90–150 tok/s	under 1 s	8K	Details
Coding questionswith Continue	Excellent	6-bit	90–150 tok/s	≈1 s	16K	Details
Coding in a repositorywith Aider	Comfortable	6-bit	80–130 tok/s	≈3 s	32K	Details
Agentic codingwith OpenCode	Comfortable	6-bit	75–130 tok/s	≈3 s	32K	Details
Long documents (64K)with Open WebUI	Excellent	6-bit	60–100 tok/s	≈9 s	64K	Details

Speeds are bandwidth-based estimates; no direct benchmark exists for this pair. “First reply” is the time to read the workload’s opening prompt. How we calculate

Get it running

Copy-paste commands for the 6-bit build with a 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF:Q6_K

This build is split into 2 files, which Ollama can't import from Hugging Face. Use llama.cpp or LM Studio instead.

Other models for MacBook Pro M5 Max 128 GB

The best-rated models for chat on this machine.

Everything MacBook Pro M5 Max 128 GB can run

Cheapest machines for Qwen3-Next 80B-A3B Instruct

Hardware rated comfortable or better for chat, lowest price first.

Same chip, other memory: 48 GB, 64 GB

Different tool, context size or workload?

These pages use typical settings. The full check models your exact setup.

Open sourceCheck my exact setup