Open-weight local LLM

Qwen 3 MoE (30B/3B active)

Efficient MoE model with only 3B active params. Fast inference at large model quality. Hybrid thinking mode. Apache 2.0.

32 GB power user 24 GB RAM Q4_K_M Fast local inference
Parameters
30B (3B active)
Minimum RAM
24 GB
Model size
18 GB
Quantization
Q4_K_M

Can Qwen 3 MoE (30B/3B active) run locally?

Qwen 3 MoE (30B/3B active) belongs on 32 GB machines when you want stronger quality without jumping to server hardware.

Search for qwen3-30b-a3b in LM Studio or another GGUF-compatible runtime.

chatcodereasoningpowerspeed

Install path

01
Check RAM fitMinimum 24 GB RAM. Start with the Q4_K_M quant.
02
Load the modelSearch qwen3-30b-a3b in LM Studio.
03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.

Strengths

  • Only 3B active params — blazing fast
  • MoE efficiency at 30B quality level
  • Hybrid thinking mode
  • Apache 2.0
  • Great balance of speed and quality

Limitations

  • MoE architecture needs more disk space
  • Not as good as dense 32B for pure quality
  • Needs 24GB RAM despite efficient inference

Best use cases

  • Fast local inference
  • Real-time chat applications
  • Code completion
  • Speed-sensitive deployments
  • Multi-user serving

Capability profile

speed
8
quality
8
coding
8
reasoning
8

Technical notes

Developer
Alibaba Cloud (Qwen Team)
License
Apache 2.0
Context window
131,072 tokens
Architecture
Mixture of Experts (MoE) — 30B total, only 3B active per token

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.

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