Local AI Advisor

Can MacBook Pro M3 Max 96 GB run Llama 4 Scout (109B-A17B)?

96 GB unified · 300 GB/s memory bandwidth · Llama 4 Scout (109B-A17B) has 108.6B parameters (17B active per token).

Short answer

Yes, comfortable for chat; borderline for agentic coding.

Best quantization for chat: 4-bit (57 GB of weights) on LM Studio. Generation: 14–19 tok/s, leaving about 26 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 WebUIComfortable4-bit14–19 tok/s≈4 s8KDetails
Coding questionswith ContinueAcceptable4-bit13–18 tok/s≈25 s16KDetails
Coding in a repositorywith AiderAcceptable4-bit11–15 tok/s≈80 s32KDetails
Agentic codingwith OpenCodeBorderline4-bit10–14 tok/s≈2 min32KDetails
Long documents (64K)with Open WebUIAcceptable4-bit7–10 tok/s≈7 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 64K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF:Q4_K_M

This build is split into 2 files, which Ollama can't import from Hugging Face. Use llama.cpp or LM Studio instead.

Different tool, context size or workload?

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

Open weights (restricted)Check my exact setup