Local AI Advisor

Can MacBook Air M4 32 GB run Qwen3.8 27B?

32 GB unified · 120 GB/s memory bandwidth · Qwen3.8 27B has 27.8B parameters.

Short answer

Yes, acceptable for chat; not recommended for coding in a repository.

Best quantization for chat: 4-bit (15 GB of weights) on LM Studio. Generation: 4.7–6 tok/s, leaving about 8.0 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 WebUIAcceptable4-bit4.7–6 tok/s≈15 s8KDetails
Coding questionswith ContinueBorderline4-bit4.6–6 tok/s≈85 s16KDetails
Coding in a repositorywith AiderTechnically Runs5-bit3.7–5 tok/s≈4 min32KDetails
Agentic codingwith OpenCodeTechnically Runs5-bit3.6–4.9 tok/s≈6 min32KDetails
Long documents (64K)with Open WebUIBorderline4-bit3.9–5 tok/s≈18 min64KDetails

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

ollama run hf.co/unsloth/Qwen3.8-27B-GGUF:UD-Q4_K_XL

Downloads 17.56 GB (UD-Q4_K_XL). Ollama's default context is small: set OLLAMA_CONTEXT_LENGTH=8192 before `ollama serve` for long prompts.

Different tool, context size or workload?

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