Local AI Advisor

Can Ryzen AI Max+ 395 mini PC / Framework Desktop · 64 GB run Llama 4 Scout (109B-A17B)?

64 GB unified · 256 GB/s memory bandwidth · Llama 4 Scout (109B-A17B) has 108.6B parameters (17B active per token).

Short answer

It loads, but it's borderline even for chat.

Best quantization for chat: Q3_K_M (50 GB of weights) on Lemonade. Generation: 16–20 tok/s, leaving about 0.0 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 WebUIBorderlineQ3_K_M16–20 tok/s≈5 s8KDetails
Coding questionswith ContinueDoes Not Fit————Details
Coding in a repositorywith AiderDoes Not Fit————Details
Agentic codingwith OpenCodeDoes Not Fit————Details
Long documents (64K)with Open WebUIDoes Not Fit————Details

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 Q3_K_M build with a 8K context. Ollama and llama.cpp download the file straight from Hugging Face.

ollama run hf.co/unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF:Q3_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