Local AI Advisor

Can MacBook Pro M4 Pro 48 GB run Qwen3-Next 80B-A3B Instruct?

48 GB unified · 273 GB/s memory bandwidth · Qwen3-Next 80B-A3B Instruct has 81.3B parameters (3B active per token).

Short answer

Yes, comfortable for chat; borderline for coding in a repository.

Best quantization for chat: 3-bit (33 GB of weights) on LM Studio. Generation: 90–120 tok/s, leaving about 4.7 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 WebUIComfortable3-bit90–120 tok/s≈1 s8KDetails
Coding questionswith ContinueAcceptable3-bit85–120 tok/s≈6 s16KDetails
Coding in a repositorywith AiderBorderline3-bit75–100 tok/s≈20 s32KDetails
Agentic codingwith OpenCodeBorderline3-bit70–95 tok/s≈25 s32KDetails
Long documents (64K)with Open WebUIBorderline3-bit50–65 tok/s≈75 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 3-bit build with a 16K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-Next-80B-A3B-Instruct-GGUF:Q3_K_M

Downloads 38.3 GB (Q3_K_M). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=16384 before `ollama serve` for long prompts.

Different tool, context size or workload?

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