LocalClaw/LLM/Llama 4 Scout (17B/109B MoE)
Open-weight local LLM

Llama 4 Scout (17B/109B MoE)

Meta Llama 4 Scout — natively multimodal MoE with 16 experts. 10M-token context window. Outperforms Gemma 3 and Mistral Small on most benchmarks at similar active cost. Llama 4 Community License.

Large-memory workstation 96 GB RAM Q4_K_M Reasoning
Run with LocalClaw Compare all models
Parameters
109B (17B active, 16 experts)
Minimum RAM
96 GB
Model size
65 GB
Quantization
Q4_K_M

Can Llama 4 Scout (17B/109B MoE) run locally?

Llama 4 Scout (17B/109B MoE) needs a serious workstation with large unified memory or high VRAM.

Search for llama-4-scout-17b-16e-instruct in LM Studio or another GGUF-compatible runtime.

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

Install path

01
Check RAM fitMinimum 96 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch llama-4-scout-17b-16e-instruct in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Meta Llama 4 Scout — natively multimodal MoE with 16 experts. 10M-token context window. Outperforms Gemma 3 and Mistral Small on most benchmarks at similar active cost. Llama 4 Community License.

Limitations

  • Performance depends on quantization, RAM bandwidth and runtime support.

Best use cases

  • chat
  • vision
  • reasoning
  • multimodal
  • power

Capability profile

speed
5
quality
9
coding
8
reasoning
9

Technical notes

Developer
llama
License
See model repository
Context window
Unknown tokens
Architecture
See model card

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.

Large unified memoryMac Studio M4 Max 128GB128GB AI PC classNVIDIA GB10 / DGX SparkHigh-memory picks128GB RAM guide

Where to go next