Tongyi-DeepResearch-30B-A3B
Tongyi-DeepResearch-30B is a 30-billion-parameter mixture-of-experts model from Alibaba that activates only 3B parameters per token. It is purpose-built for agentic tasks requiring long-horizon reasoning and information-seeking (e.g., web research, multi-step problem solving). The model is open-source under Apache 2.0, ungated, and compatible with ReAct and a heavier 'IterResearch' inference mode for test-time scaling.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Alibaba-NLP |
| Parameters | 30.5B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 65.8k |
| Likes | 812 |
| Last updated | 2025-10-10 |
| Source | Alibaba-NLP/Tongyi-DeepResearch-30B-A3B |
What Tongyi-DeepResearch-30B-A3B is
A Qwen3-based MoE architecture with 30.5B total parameters and selective activation (3B active per token). Trained via: (1) synthetic data generation pipeline for agentic pretraining, (2) large-scale continual pretraining on agentic interaction data, (3) end-to-end reinforcement learning using Group Relative Policy Optimization with token-level gradients. Inference supports ReAct (core ability evaluation) and IterResearch 'Heavy' mode (test-time scaling). Context length unknown. Compatible with Hugging Face transformers, safetensors, and Azure deployment.
Run Tongyi-DeepResearch-30B-A3B locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Alibaba-NLP/Tongyi-DeepResearch-30B-A3B")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: 60–120 GB GPU VRAM (unquantized, float16/bfloat16). MoE architecture with 3B active parameters may allow partial loading optimizations. Quantization (int8, int4) can reduce footprint to ~15–30 GB. Multi-GPU setups recommended for inference throughput. CPU inference not practical at this scale.
Model card does not explicitly discuss LoRA, QLoRA, or fine-tuning feasibility. Likely fine-tunable given Qwen3 base and Hugging Face transformers compatibility, but specific guidance missing. Recommend consulting GitHub repository (https://github.com/Alibaba-NLP/DeepResearch) for training scripts and adapter support. Reinforcement learning framework mentioned (GRPO) suggests RL fine-tuning is possible but may require specialized setup.
When to avoid it — and what to weigh
- Need guaranteed real-time latency — IterResearch 'Heavy' mode uses test-time scaling (longer inference time for better quality). ReAct mode is faster but may trade depth. If sub-100ms latency is critical, validate performance empirically first.
- Limited deployment infrastructure — Even with 3B active parameters, total model size (~30B) requires substantial GPU VRAM (estimate 60–120 GB unquantized depending on precision). Requires modern hardware or quantization. Limited suitable for edge or CPU-only deployments.
- Out-of-domain tasks unrelated to search/research — Model is specifically optimized for agentic search and long-horizon reasoning. General-purpose tasks (classification, summarization, translation) may underperform compared to broader LLMs. Benchmark performance reported only on agentic tasks.
- Production use without validation of benchmarks — Benchmarks (GAIA, BrowserComp, etc.) are stated but not detailed in the card. Actual performance on your specific task unknown. Requires thorough evaluation before production deployment.
License & commercial use
Apache License 2.0 (OSI-approved, permissive). Allows commercial use, modification, and distribution with proper attribution and liability disclaimer. No proprietary restrictions stated.
Apache 2.0 is permissive and explicitly allows commercial use. Model is ungated and freely downloadable. No evidence of restrictions on commercial deployment. However: (1) verify Alibaba's policy on derivative works; (2) ensure compliance with any downstream dependencies (e.g., training data sources); (3) test thoroughly for your use case before committing to production, as benchmark performance is agentic-specific and may not transfer.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No explicit security audit, data provenance statement, or adversarial robustness evaluation provided. Model trained on synthetic + real agentic interaction data; source and filtering criteria unknown. Agentic inference (web browsing, action execution) introduces risk of prompt injection and misuse; enforce guardrails at application level. Quantization may degrade robustness slightly. No known vulnerabilities stated. Requires risk assessment before deploying in high-stakes or public-facing systems.
Alternatives to consider
GPT-4o / Claude 3.5 Sonnet (proprietary)
Broader capabilities, stronger general reasoning, no deployment burden. Higher cost and closed-source. Better for organizations prioritizing capability over cost and control.
Qwen2.5-72B or Llama 3.1-70B (dense open-source)
Larger dense models with broader training. May outperform on non-agentic tasks and general reasoning. Higher VRAM footprint; no MoE efficiency. Consider if your workload is diverse.
Agent frameworks + smaller model (e.g., LangChain + Mistral 7B)
Lower hardware cost and latency. May require more orchestration and tool prompting to match Tongyi's agentic performance. Better for resource-constrained deployments.
Ship Tongyi-DeepResearch-30B-A3B with senior software developers
Ready to build autonomous research agents or deep-reasoning applications? Tongyi-DeepResearch is open-source, commercially permissive, and optimized for long-horizon information-seeking tasks. Start with the GitHub repository (Alibaba-NLP/DeepResearch) for inference scripts, or contact our team to architect a scalable deployment on your infrastructure.
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Tongyi-DeepResearch-30B-A3B FAQ
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Custom software development services
From first prototype to production, DEV.co delivers software development services around tools like Tongyi-DeepResearch-30B-A3B. Our software development agency staffs experienced software developers and web developers for custom software development, web development, integrations, and ongoing support across open-source llms and beyond.
Deploy Tongyi-DeepResearch for Your Agentic AI Stack
Ready to build autonomous research agents or deep-reasoning applications? Tongyi-DeepResearch is open-source, commercially permissive, and optimized for long-horizon information-seeking tasks. Start with the GitHub repository (Alibaba-NLP/DeepResearch) for inference scripts, or contact our team to architect a scalable deployment on your infrastructure.