Local AI Advisor

Can MacBook Pro M4 Max 128 GB run Llama 4 Scout (109B-A17B)?

128 GB unified · 546 GB/s memory bandwidth · Llama 4 Scout (109B-A17B) has 108.6B parameters (17B active per token).

Short answer

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

Best quantization for chat: 4-bit (57 GB of weights) on LM Studio. Generation: 25–30 tok/s, leaving about 58 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 WebUIExcellent4-bit25–30 tok/s≈2 s8KDetails
Coding questionswith ContinueComfortable5-bit18–25 tok/s≈15 s16KDetails
Coding in a repositorywith AiderAcceptable6-bit14–19 tok/s≈50 s32KDetails
Agentic codingwith OpenCodeAcceptable5-bit15–20 tok/s≈70 s32KDetails
Long documents (64K)with Open WebUIAcceptable5-bit11–15 tok/s≈4 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 5-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:Q5_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