Local AI Advisor

Can Mac Studio M3 Ultra 512 GB run Qwen3-Next 80B-A3B Instruct?

512 GB unified · 819 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: 100–130 tok/s, leaving about 439 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.

WorkloadVerdictQuantGenerationFirst replyContext
Chatwith Open WebUIExcellent6-bit100–130 tok/sunder 1 s8KDetails
Coding questionswith ContinueExcellent6-bit95–130 tok/s≈2 s16KDetails
Coding in a repositorywith AiderComfortable6-bit85–120 tok/s≈5 s32KDetails
Agentic codingwith OpenCodeComfortable6-bit85–110 tok/s≈7 s32KDetails
Long documents (64K)with Open WebUIExcellent6-bit65–90 tok/s≈20 s64KDetails

Speeds are estimates calibrated against measured benchmarks on similar hardware. “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.

Different tool, context size or workload?

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