Can MacBook Pro M5 32 GB run Qwen3 8B?

Q: Can MacBook Pro M5 32 GB run Qwen3 8B?

Yes, comfortable for chat; borderline for agentic coding. Rated by Local AI Advisor's engine at 6-bit, about 10–17 tok/s for chat.

32 GB unified · 153 GB/s memory bandwidth · Qwen3 8B has 8.2B parameters.

Short answer

Yes, comfortable for chat; borderline for agentic coding.

Best quantization for chat: 6-bit (6.4 GB of weights) on LM Studio. Generation: 10–17 tok/s, leaving about 16 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	Comfortable	6-bit	10–17 tok/s	≈1 s	8K	Details
Coding questionswith Continue	Acceptable	6-bit	10–16 tok/s	≈8 s	16K	Details
Coding in a repositorywith Aider	Acceptable	6-bit	8–14 tok/s	≈25 s	32K	Details
Agentic codingwith OpenCode	Borderline	6-bit	8–13 tok/s	≈40 s	32K	Details
Long documents (64K)with Open WebUI	Acceptable	8-bit	6–9 tok/s	≈65 s	32K	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 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 M5 32 GB

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

Same chip, other memory: 16 GB, 24 GB

Different tool, context size or workload?

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

Open sourceCheck my exact setup