Can MacBook Pro M3 Max 36 GB run Qwen3 8B?

36 GB unified · 300 GB/s memory bandwidth · Qwen3 8B has 8.2B parameters.

Short answer

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

Best quantization for chat: 6-bit (6.4 GB of weights) on LM Studio. Generation: 25–35 tok/s, leaving about 20 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	25–35 tok/s	≈2 s	8K	Details
Coding questionswith Continue	Comfortable	6-bit	25–35 tok/s	≈10 s	16K	Details
Coding in a repositorywith Aider	Acceptable	8-bit	17–20 tok/s	≈35 s	32K	Details
Agentic codingwith OpenCode	Acceptable	8-bit	16–20 tok/s	≈55 s	32K	Details
Long documents (64K)with Open WebUI	Acceptable	8-bit	14–19 tok/s	≈2 min	32K	Details

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 8-bit build with a 32K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Qwen3-8B-GGUF:Q8_0

Downloads 8.71 GB (Q8_0). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=32768 before `ollama serve` for long prompts.

Other models for MacBook Pro M3 Max 36 GB

The best-rated models for chat on this machine.

Everything MacBook Pro M3 Max 36 GB can run

Cheapest machines for Qwen3 8B

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

Same chip, other memory: 96 GB

Different tool, context size or workload?

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

Open sourceCheck my exact setup