LocalClaw/LLM/Llama 4 Scout (17B/16E MoE)
Open-weight local LLM

Llama 4 Scout (17B/16E MoE)

Meta's multimodal MoE model. 17B active params across 16 experts (~109B total). Built-in image understanding. 10M token context window. Apache 2.0. 728K downloads.

16 GB sweet spot 16 GB RAM Q4_K_M Multimodal chat
Run with LocalClaw Compare all models
Parameters
17B active (109B total, 16 experts)
Minimum RAM
16 GB
Model size
10 GB
Quantization
Q4_K_M

Can Llama 4 Scout (17B/16E MoE) run locally?

Llama 4 Scout (17B/16E MoE) is a practical pick for 16 GB machines, especially with Q4_K_M quantization.

Search for llama-4-scout in LM Studio or another GGUF-compatible runtime.

meta-llama/Llama-4-Scout-17B-16E-Instruct-GGUF
chatvisionpowergeneral

Install path

01
Check RAM fitMinimum 16 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch llama-4-scout in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Latest Meta multimodal model
  • Built-in vision
  • MoE for efficient inference
  • 728K downloads

Limitations

  • Needs 16GB RAM
  • New model — less community support
  • MoE complexity

Best use cases

  • Multimodal chat
  • Image understanding
  • General AI tasks
  • Content creation

Capability profile

speed
6
quality
8
coding
8
reasoning
8

Technical notes

Developer
Meta AI
License
Llama 4 Community License
Context window
131,072 tokens
Architecture
Mixture of Experts (MoE) with native vision

This model fits these next steps

Hardware fit is based on LocalClaw's RAM tier, model size and quantization metadata. Always leave memory headroom for your OS and runtime.

Starter desktopMac mini M4 16GBPortable fitMacBook Air M4 16GBBest 16GB models16GB RAM guide

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