Open-Weight LLM · Private & Custom AI
llm-jp-4-32b-a3b-thinking
Japanese/English-focused MoE model for private deployment in ops workflows: multilingual reasoning and task automation without vendor lock-in.
LLM-jp-4-32b-a3b-thinking is a 32B Mixture-of-Experts transformer trained by Japan's National Institute of Informatics on 11.7T tokens, optimized for bilingual (JA/EN) multi-turn reasoning. Built for companies running private infrastructure who need Japanese-language capability and full data control in ops automation and custom AI applications.
Model facts
Private deployment
Run llm-jp-4-32b-a3b-thinking in your own environment
Self-hosting is native to this model's design. Estimated ~40–80 GB VRAM (depending on quantization) for inference. Companies deploy it locally to keep Japanese/English operational data, customer interactions, and proprietary workflows private. Apache 2.0 permissive license enables unrestricted private forks, fine-tuning, and integration into internal ops systems without vendor approval.
Operational AI use cases
Japanese Customer Support & Ticket Automation
Route, summarize, and draft responses for support tickets in Japanese and English. The model's bilingual training and reasoning capability (tuned with DPO) enable coherent multi-turn conversations. Run locally to keep customer data and conversation logs inside your environment; no third-party access to sensitive support interactions.
Internal Process Documentation & Knowledge Synthesis
Ingest procedural docs, internal wikis, and runbooks in both languages. Use the model to generate multilingual Standard Operating Procedures, FAQ sections, and onboarding guides. MoE architecture (8 of 128 experts active per token) reduces compute cost per operation, making batch doc generation economical.
Compliance & Risk Assessment for Japanese Operations
Analyze contracts, regulatory updates, and risk logs in Japanese without sending them to a cloud API. The model was evaluated on Japanese safety benchmarks (AnswerCarefully) and shows grounded reasoning. Deploy it as a screening and summarization layer for legal/ops teams before escalation to humans.
Custom AI
As a base for custom AI
Strong base for custom applications targeting Japanese or multilingual audiences. The post-training pipeline (SFT + DPO, no RL) means the model is controllable and fine-tunable without reward-model complexity. Available training datasets (SFT and DPO) are open, allowing companies to adapt the model to domain-specific terminology, internal jargon, and customer-facing tone without retraining from scratch.
In the operating system
Where it fits
Knowledge and agent layer in an AI OS. Use it as the core reasoning engine for routing, extraction, and planning in ops workflows. MoE structure allows conditional scaling—activate only the needed experts for lightweight tasks (support triage) and full inference for complex reasoning (multi-turn diagnosis). Pair with retrieval or vector stores for grounded internal knowledge systems.
Data control & security
Private deployment means operational data—tickets, logs, contracts, customer metadata—remains in your infrastructure. No transmission to external APIs, no third-party logging. Apache 2.0 license and self-hosted architecture give you audit control and the ability to log inference for compliance. Note: self-hosting does not inherently make the model 'secure'—your deployment architecture (network isolation, access controls, encryption) determines actual security posture.
Hardware footprint
Estimate: ~40–50 GB VRAM (fp16), ~20–28 GB (int8 quantization), ~12–16 GB (int4 quantization, e.g., GPTQ). MoE activation reduces per-token compute vs. dense model of equivalent capacity. Exact numbers depend on inference framework, batch size, and context length used.
Integration
Tokenizer is Unigram byte-fallback; use the included tokenizer (not generic OpenAI compatibility). Chat template is OpenAI Harmony-compatible but requires the model's tokenizer for correct behavior. Integrate via standard HuggingFace transformers pipeline or ONNX export. Supports long context (65,536 tokens), useful for multi-document workflows. Refer to llm-jp-4-cookbook for example code for custom chat, streaming, and batching.
When it's not the right fit
- —English-only ops teams or single-language use cases—overkill for monolingual workloads; smaller English models may be more cost-efficient.
- —Real-time ultra-low-latency applications (<100ms) at scale—MoE routing overhead and 65k context require careful batching; latency unpredictable.
- —When state-of-the-art reasoning is non-negotiable—evaluation shows ~7.8/10 MT-Bench (medium reasoning_effort); LLaMA 3.1, Mixtral, or proprietary models may outperform on complex logic.
- —Heavy customization via reinforcement learning—post-training used SFT+DPO only, no RL infrastructure provided; RLHF alignment requires external tooling.
Alternatives to consider
Mixtral 8x7B (MoE-Instruct)
Proven open MoE, English-centric, smaller footprint; lacks Japanese tuning and multilingual reasoning. Better if you don't need Japanese.
LLaMA 3.1 70B
Larger, denser, stronger English reasoning; requires more VRAM; no Japanese-specific training. Better for pure-English ops if you have GPU capacity.
Qwen2.5 32B
Compact 32B dense model, multilingual, good ops fit; less MoE efficiency; alternative if you prefer dense models or need Chinese support.
Related open models
FAQ
Can we run this entirely on-premises without internet access?
Yes. Download weights and tokenizer once, then deploy on your infrastructure. No phone-home, no license validation, no API calls required—Apache 2.0 allows it. You manage versioning, updates, and access.
Is this model commercially usable?
Yes. Apache 2.0 license permits commercial use, modification, and redistribution without attribution (though attribution is encouraged). You can build and sell products using it as long as you include a copy of the license in distribution.
How does the MoE architecture help with ops automation?
Only 8 of 128 experts activate per token, reducing compute and latency per inference step. For high-volume ops tasks (ticket triage, doc summarization), this translates to lower per-inference cost and faster batch processing than a 32B dense model.
Can we fine-tune it on our own ops data?
Yes. Training datasets used for SFT and DPO are publicly available on HuggingFace. You can apply LoRA, QLoRA, or full fine-tuning with your domain data. No licensing restrictions on outputs. Reference the llm-jp-4-cookbook for examples.
Build Private, Multilingual AI for Your Operations
LLM-jp-4-32b is production-ready for Japanese/English ops workflows. LLM.co helps you integrate this model into a complete AI OS: custom fine-tuning, private deployment, and ops automation. Let's build your private AI stack—no vendor lock-in, full control.