Open-weight local LLM
Llama 4 Maverick (17B/400B MoE)
Meta Llama 4 Maverick — 128-expert MoE flagship. Matches or beats GPT-4o and Gemini 2.0 Flash on reasoning, coding and multimodal benchmarks. 1M-token context. Server-grade hardware only. Llama 4 Community License.
Server-grade
384 GB RAM
Q4_K_M
Reasoning
Parameters
400B (17B active, 128 experts)
Minimum RAM
384 GB
Model size
240 GB
Quantization
Q4_K_M
Can Llama 4 Maverick (17B/400B MoE) run locally?
Llama 4 Maverick (17B/400B MoE) is server-grade locally. Keep it for comparison unless you have very large unified memory, multiple GPUs or remote inference.
Search for llama-4-maverick-17b-128e-instruct in LM Studio or another GGUF-compatible runtime.
meta-llama/Llama-4-Maverick-17B-128E-Instructchatvisionreasoningmultimodalquality
Install path
01
Check RAM fitMinimum 384 GB RAM. Start with the Q4_K_M quant.02
Load the modelSearch llama-4-maverick-17b-128e-instruct in LM Studio.03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.Strengths
- Meta Llama 4 Maverick — 128-expert MoE flagship. Matches or beats GPT-4o and Gemini 2.0 Flash on reasoning, coding and multimodal benchmarks. 1M-token context. Server-grade hardware only. Llama 4 Community License.
Limitations
- Performance depends on quantization, RAM bandwidth and runtime support.
Best use cases
- chat
- vision
- reasoning
- multimodal
- quality
Capability profile
Technical notes
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.