Local AI Advisor

Can Mac Studio M3 Ultra 96 GB run Qwen3 8B?

96 GB unified · 819 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: 45–60 tok/s, leaving about 78 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-bit45–60 tok/sunder 1 s8KDetails
Coding questionswith ContinueComfortable6-bit45–60 tok/s≈4 s16KDetails
Coding in a repositorywith AiderAcceptable8-bit30–40 tok/s≈15 s32KDetails
Agentic codingwith OpenCodeAcceptable8-bit25–35 tok/s≈20 s32KDetails
Long documents (64K)with Open WebUIAcceptable8-bit25–35 tok/s≈35 s32KDetails

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.

Different tool, context size or workload?

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