Open-weight MoE
MiroThinker 1.7 Mini (30B MoE)
⚠️ Despite the "Mini" name, this is a full 30B MoE model (Qwen3-30B-A3B). 3B = active params per forward pass, NOT model size. ~82 GB full weights. Requires H100 80GB or multi-GPU. 256K context, multilingual (EN/ZH+), deep-research agent with tool calls. Released 11 Mar 2026. Apache 2.0.
64 GB workstation
48 GB RAM
Q4_K_M
Coding assistant
Parameters
30B (3B active, MoE)
Minimum RAM
48 GB
Model size
18 GB
Quantization
Q4_K_M
Can MiroThinker 1.7 Mini (30B MoE) run locally?
MiroThinker 1.7 Mini (30B MoE) is best for 64 GB workstations and larger Apple Silicon or NVIDIA setups.
Search for mirothinker-1.7-mini in LM Studio or another GGUF-compatible runtime.
miromind-ai/MiroThinker-1.7-minireasoningcodepowerquality
Install path
01
Check RAM fitMinimum 48 GB RAM. Start with the Q4_K_M quant.02
Load the modelSearch mirothinker-1.7-mini in LM Studio.03
Control locallyUse LocalClaw to manage models, agents, chat, channels and scheduled OpenClaw work.Strengths
- ⚠️ Despite the "Mini" name, this is a full 30B MoE model (Qwen3-30B-A3B). 3B = active params per forward pass, NOT model size. ~82 GB full weights. Requires H100 80GB or multi-GPU. 256K context, multilingual (EN/ZH+), deep-research agent with tool calls. Released 11 Mar 2026. Apache 2.0.
Limitations
- Performance depends on quantization, RAM bandwidth and runtime support.
Best use cases
- reasoning
- code
- power
- 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.