Open-Weight LLM · Private & Custom AI
Tongyi-DeepResearch-30B-A3B
A 30B MoE agent model designed for research automation and long-horizon agentic workflows—deploy privately to control data while automating complex information-seeking tasks.
Tongyi-DeepResearch is a mixture-of-experts (MoE) text-generation model with 30B total parameters but only 3B active per token, built for agentic reasoning and deep-research tasks. For ops teams, it's a foundation for automating multi-step research, investigation, and decision-support workflows that stay entirely within your own infrastructure. The active-parameter efficiency makes private deployment feasible on mid-tier hardware.
Model facts
Private deployment
Run Tongyi-DeepResearch-30B-A3B in your own environment
Self-host by downloading model weights (safetensors format available) and running the inference scripts from Alibaba-NLP's GitHub repo. The MoE architecture with selective activation reduces memory footprint vs. dense 30B models—roughly 24–32 GB VRAM for full-precision inference (estimate). Private deployment keeps all query data, research outputs, and agent interactions within your environment, eliminating data exposure to third-party APIs. Suitable for companies handling proprietary research, competitive intelligence, or regulated operational queries.
Operational AI use cases
Internal Research & Due Diligence Automation
Deploy as a private agent to automate vendor research, regulatory monitoring, and competitive analysis. The model's agentic paradigm (ReAct + IterResearch) enables it to simulate research workflows—formulating queries, synthesizing findings, and reasoning over multiple information sources—without exfiltrating sensitive business context to public APIs.
Support & Knowledge-Base Investigation
Use as a backbone for internal support automation. The model excels at long-horizon reasoning tasks, making it suitable for routing complex support tickets, investigating customer issues across internal wikis and systems, and generating investigative summaries—all within your private infrastructure.
Compliance & Risk Documentation
Automate deep-dive reviews of policies, regulations, and internal risk assessments. The model's research-oriented design lets ops teams query regulatory databases and internal documentation, synthesizing findings into audit-ready summaries while keeping sensitive compliance data private.
Custom AI
As a base for custom AI
Strong foundation for building proprietary research agents and agentic automation products. The model's compatibility with ReAct and IterResearch inference paradigms means you can customize the reasoning loop, integrate it with internal APIs/databases, and fine-tune on domain-specific research tasks (synthetic data generation pipeline noted in model card suggests continued pre-training is feasible). Best fit: companies building internal AI research assistants, compliance automation, or knowledge-work automation products.
In the operating system
Where it fits
Acts as the **agentic reasoning layer** in an ops AI stack. Sits between workflow orchestration (agents, ReAct loops) and knowledge backends (internal databases, document stores). In an LLM.co-style system, it's the core 'think' component—managing multi-step research, decision reasoning, and information synthesis before handing results to operational systems (ticketing, compliance, reporting).
Data control & security
Self-hosting ensures all queries, research artifacts, and agent reasoning remain in your environment—no API calls home, no third-party data access. This is an **architectural choice**, not a model feature: you control infrastructure, encryption, and access logs. No built-in security guarantees from the model itself; you are responsible for network isolation, authentication, and audit controls. Suitable for handling proprietary research and regulated operational workflows.
Hardware footprint
**Estimate:** - Full precision (FP32): ~122 GB VRAM (dense equivalent; MoE sparse activation reduces active compute) - FP16 (recommended): ~61 GB VRAM - 8-bit quantized: ~30–32 GB VRAM (practical for production) MoE efficiency gains are in *compute* (3B active tokens), not VRAM footprint—weights still loaded. Suitable for A100 (80GB) or dual H100 setups; smaller clusters may require tensor parallelism.
Integration
Inference via transformers library (safetensors compatible, no gating). The GitHub repo includes example scripts for ReAct and IterResearch inference modes. Integration points: wrap with FastAPI or similar to connect to internal tools (Slack, ticketing, document stores, APIs). MoE structure means batching strategies matter for throughput optimization. Requires orchestration layer to manage multi-step agentic loops and persist results to ops systems.
When it's not the right fit
- —Latency-critical applications: agentic inference (ReAct + test-time scaling) is intentionally slow; IterResearch 'Heavy' mode trades speed for reasoning depth.
- —Unsupervised general-purpose chat: model is optimized for research/investigation, not conversational assistants. May be overfit to agentic patterns.
- —Real-time decision-making at scale: MoE routing overhead and multi-step agent loops conflict with sub-100ms SLA requirements.
- —Offline-only environments without GPU: the 30B parameter count requires significant compute; CPU inference is impractical.
Alternatives to consider
Llama 3.1 (70B) via Meta
Larger, general-purpose dense model; no MoE overhead; better for traditional fine-tuning. Trade-off: ~2x VRAM cost, no built-in agentic optimization.
Mixtral 8x22B (Mistral)
Also MoE-based with active-token efficiency; broader capability spectrum. Trade-off: less specialized for research/agentic tasks; fewer published benchmarks on agent workflows.
Qwen2.5-32B (Alibaba)
Same developer ecosystem, dense 32B model with broader use-case coverage. Trade-off: no MoE efficiency; not optimized for agentic reasoning.
Related open models
FAQ
Can we fine-tune this model on our internal data while keeping it private?
Yes. Download the model weights, run fine-tuning scripts on your own infrastructure (the model card mentions a 'fully automatic synthetic data generation pipeline'). This is the primary private-AI path. You'll need GPU clusters and orchestration, but data never leaves your environment. Requires engineering effort to set up supervised fine-tuning or RL loops (Group Relative Policy Optimization framework noted).
What's the commercial/licensing story?
Apache 2.0 license (permissive). No gating, no restrictions on commercial use. You can deploy privately, build products on top, and sell applications using this model. Read the full license; generally, Apache 2.0 requires attribution in documentation but allows proprietary derivatives.
How does 'test-time scaling' (IterResearch mode) affect ops workflows?
Test-time scaling means inference is slower but reasoning is deeper. Great for high-stakes research/compliance tasks where accuracy > speed. Poor fit for real-time support chatbots. Plan for 5–30s inference per query in IterResearch mode (vs. <1s in ReAct mode). Build async workflows or batch jobs, not synchronous APIs.
Does this model include safety/alignment guarantees?
Unknown from the card. The model is trained on agentic data and RL (Group Relative Policy Optimization), but no safety tuning details are published. Treat as a research-optimized base model, not a production-safe system. You are responsible for safety testing, output validation, and guardrails in your deployment.
Build Private Research Agents on Your Infrastructure
Tongyi-DeepResearch is built for agentic automation—deploy it within LLM.co to orchestrate custom research, compliance, and ops workflows that stay entirely in your environment. Let's architect a system that turns deep reasoning into operational advantage.