LocalClaw/LLM/LLM-jp-4.1 32B-A3B Thinking
Open-weight MoE

LLM-jp-4.1 32B-A3B Thinking

Use case: Coding assistant. Check the memory fit and choose a setup path below.

48 GB catalogue minimum48 GB RAMQ4_K_MCoding assistant
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LM StudioNot available for this model
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OllamaNot available for this model
Open on Hugging FaceFiles, licence and available downloads
llama.cppNot available for this model
Use with LocalClawOptional workspace after the model is installed
This model needs its official runtimeLLM-jp llama.cpp fork
Parameters
32B (3.8B active, MoE)
Minimum RAM
48 GB
Model size
19.17 GB
Quantization
Q4_K_M

About LLM-jp-4.1 32B-A3B Thinking

Catalogue summary: Official Apache 2.0 LLM-jp 4.1 sparse MoE reasoning model with 32B total / 3.8B active parameters, 65K context, Japanese-English post-training and an official Q4_K_M GGUF path for larger local workstations.

LLM-jp-4.1 32B-A3B Thinking has a catalogue minimum of 48 GB RAM with Q4_K_M. Actual memory use and speed vary by context length, runtime, backend and system headroom.

Use the official LLM-jp llama.cpp fork setup. The current low-bit files are not a stock LM Studio install.

Model sourcePublic GGUF repository (verified 2026-11-06): llm-jp/llm-jp-4.1-32b-a3b-thinking-ggufUpstream releasePaper or model cardLicence text
chatcodereasoninglong-contextmultilingualpower

Install path

01
Check RAM fitMinimum 48 GB RAM. Start with the Q4_K_M quant.
02
Load the modelFollow the official LLM-jp llama.cpp fork instructions. Stock LM Studio support is not confirmed.
03
Try your workloadStart with a short prompt, check memory use and adjust context length before running longer tasks.

Catalogue record

  • Family: llm-jp
  • Parameters: 32B (3.8B active, MoE)
  • Recommended quantization: Q4_K_M
  • Catalogue minimum RAM: 48 GB
  • Catalogue model size: 19.17 GB
  • Tags: chat, code, reasoning, long-context, multilingual, power

Practical limits

  • Catalogue RAM is a minimum estimate, not a guarantee for every context length or runtime.
  • Speed and memory use vary by quantization, backend, context length and system headroom.
  • Verify architecture, licence and usage restrictions in the linked upstream material before deployment.

Reading the scores

LocalClaw /10 is an editorial catalogue rating. Community /5 comes from member reviews. Your machine fit is a separate memory and runtime assessment.

Capability profile

Repository catalogue ratings used by LocalClaw's editorial rubric. They are not a standardized third-party benchmark.

speed
5
quality
8
coding
8
reasoning
9

Technical notes

Developer
LLM-jp
License
Apache 2.0
Context window
65,536 tokens
Architecture
Sparse 32.14B-parameter MoE transformer from the LLM-jp-4.1 thinking series, with 32 layers, 128 routed experts, 8 active experts, about 3.83B active parameters per token, 40 attention heads and a 65,536-token context window.

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.

Workstation fitMac Studio M4 Max 64GBCUDA workstation classNVIDIA GB10 / DGX SparkLarge local models64GB RAM guide

Related catalogue entries

Linked mechanically by family, shared tags and nearby RAM tier; this is not a quality ranking.

LLM-jp-4 33B Thinking 33BLLM-jp-4.1 8B Thinking 8BKimi Linear 48B-A3B Instruct 48B (3B active, MoE)Nemotron 3.5 Lightning 30B-A3B 30B (3B active, MoE)Ornith-1.5-35B-A3B 35B (3B active, MoE)Mistral Small 4 (119B-A6.5B) 119B (6.5B active, MoE)Laguna XS 2.1 33B (3B active, MoE)K2-Horizon-32B 32B dense

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