Open-Weight LLM · Private & Custom AI
Qwen-AgentWorld-35B-A3B
A native language world model for simulating agentic environments across 7 operational domains—tool use, search, terminals, software engineering, mobile, web, and OS—enabling companies to build private AI agents that understand and predict system state changes.
Qwen-AgentWorld-35B-A3B is a 35B-parameter mixture-of-experts model (3B activated) trained specifically to predict environment state transitions given agent actions and interaction history. Unlike general-purpose LLMs adapted post-hoc for agentic work, this model bakes environment modeling into its training objective from continual pre-training onward. For ops teams, it means a foundation for building private, controllable AI agents that can simulate and reason through multi-step workflows without external APIs.
Model facts
Private deployment
Run Qwen-AgentWorld-35B-A3B in your own environment
The model is deployable via vLLM, SGLang, or Hugging Face Transformers. Self-hosting requires approximately 70–140 GB VRAM depending on precision (fp16 vs. int8), distributed across 4+ GPUs for reasonable throughput. A company deploying privately retains full data control: all interaction logs, environment states, and agent reasoning stay within their infrastructure. This is critical for regulated industries (finance, healthcare, manufacturing) where agent traces and system state data cannot leave the boundary. The 262K context window allows multi-turn simulation without external API calls, but OOM errors may require reducing context length to 128K or below on constrained hardware.
Operational AI use cases
Autonomous IT/SysOps Ticket Routing & Simulation
Route and simulate resolution of operational tickets (Jira, ServiceNow) by predicting terminal command outputs, system state changes, and log entries. The Terminal domain support lets ops teams build private agents that execute diagnostic workflows (log parsing, service restarts, disk checks) and predict outcomes before human approval—reducing MTTR and keeping sensitive system traces off public APIs.
Software Engineering & Code Review Automation
Simulate code repository changes, build outputs, and CI/CD pipeline state. Use the SWE domain to predict test results, dependency resolutions, or code refactoring impacts without running actual builds. Useful for pre-flight checks, automated code review assistance, and build failure prediction in private GitLab/GitHub environments.
Customer Support Workflow Simulation & Agent Training
Simulate web interaction sequences, search result chains, and tool-calling flows common in support. Train private support agents to predict customer journey outcomes (successful account recovery, escalation paths) and optimize prompts/workflows before deploying to live chat systems. Keeps customer interaction traces private and reduces external LLM API costs.
Custom AI
As a base for custom AI
Strong candidate for building bespoke agentic products. The native world model architecture means teams can fine-tune on domain-specific environments (e.g., custom internal APIs, proprietary manufacturing equipment states, insurance claim workflows) without starting from a general-purpose LLM. The RL-trained reasoning chains and 262K context window support long, complex state prediction tasks. Companies can use the base model as a foundation, further tune on private interaction logs, and deploy a fully internal AI agent that understands their environment's dynamics.
In the operating system
Where it fits
In an AI operating system, this sits at the agent/simulation layer. It's not a knowledge retriever (no RAG-optimized embeddings) or a fine-grained document classifier. It's the reasoning core for agentic workflows: given state + action, predict next state. Pair it with a workflow orchestrator (to issue commands), a knowledge layer (to ground facts), and a feedback loop (to close the agent loop). It excels at replacing rule-based state machines or expensive external API calls for environment simulation.
Data control & security
Self-hosting this model ensures that all agent reasoning, environment state traces, and interaction histories remain within your infrastructure. No interaction data flows to Qwen's servers or third-party APIs. This is an architectural advantage for companies subject to data residency laws (GDPR, HIPAA, SOC 2). However, the model itself is not certified secure or compliant—data security depends on your deployment environment (network isolation, access controls, encryption at rest/transit). The model card contains no security audit or privacy certification; treat self-hosting as a data-locality strategy, not a compliance guarantee.
Hardware footprint
Estimated VRAM by precision: **fp16** ~70–90 GB (4× H100/A100), **int8** ~35–50 GB (2× H100/A100), **bnb 4-bit** ~20–30 GB (single A100). These assume tensor parallelism across multiple GPUs. Single-GPU inference is not practical at full context length. vLLM/SGLang handle quantization and kernel optimization; actual footprint varies by batch size and max_model_len setting.
Integration
Expose via OpenAI-compatible API (vLLM, SGLang) for easy integration with existing orchestrators (LangChain, LlamaIndex, Crew AI). The model accepts chat format messages; wrap with domain-specific system prompts (provided in GitHub repo) for terminal, web, SWE, etc. Expect ~4-8s latency per prediction on A100 with tensor parallelism. Integrate with your action execution layer (bash, HTTP, browser automation) to close the loop. State representation must be explicit in prompts—the model predicts *textual* environment outputs, so you control input fidelity.
When it's not the right fit
- —You need real-time, sub-second latency. The 35B parameter count + extended context makes inference slower than smaller models; batch 1 latency is ~4–8s on good hardware.
- —Your operational domain is outside the seven covered (MCP, Search, Terminal, SWE, Android, Web, OS). Zero-shot generalization is claimed but unproven on entirely novel environments; expect performance degradation.
- —You cannot afford GPU infrastructure. Self-hosting requires multiple high-end GPUs; running on CPU or small edge devices is infeasible.
- —Your environment interactions are highly visual (GUI screenshots, complex UX). The model card mentions visual component definitions but only language weights are included; visual grounding is unsupported.
Alternatives to consider
Qwen3.5-35B-A3B-Base (untuned)
Same parameter count and activation profile but no agentic or world-model tuning. Useful if you want to fine-tune on your own domain traces; AgentWorld is pre-tuned for 7 domains, so choose based on whether your workflow aligns.
DeepSeek-V4 (671B MoE, open)
Larger parameter count, stronger general reasoning, but not specialized for environment simulation. Better for knowledge-heavy tasks; requires more VRAM. Slower deployment, no domain-specific training.
Llama 3.1 405B or 70B
General-purpose instruction-tuned models with strong coding/reasoning. No world-model or agentic specialization. Cheaper to run at 70B scale, but you'd need extensive fine-tuning to match AgentWorld's environment simulation capability.
Related open models
FAQ
Can I deploy this model entirely on-premises without cloud APIs?
Yes. Deploy via vLLM or SGLang on your own GPU hardware. All reasoning and state prediction stays local. You control prompts, system prompts, and environment inputs fully. No external API calls are required (the training data excludes external API outputs per the model card).
Is this model licensed for commercial use in a product?
Yes. Apache-2.0 permits commercial use, redistribution, and modifications without royalty. You can build and sell a product using this model. Attribute Qwen as required by Apache-2.0. Review the full license terms if you plan to modify and redistribute the model itself.
What if I have domain-specific environments not in the seven covered domains?
The model is trained on the seven domains but may generalize via zero-shot. Performance is unproven for out-of-domain environments (though the paper claims generalization to OpenClaw). Test empirically on your environment. Alternatively, fine-tune on your own traces using the SFT+RL pipeline—the base model provides a strong starting point.
How much does the 262K context window actually help for multi-turn agent workflows?
Long context reduces the need to summarize or forget earlier interactions, allowing the model to maintain state consistency across many agent steps. The model card notes that shorter contexts degrade simulation fidelity; maintain at least 128K tokens if possible. In practice, expect OOM errors on single A100s; plan for multi-GPU serving.
Build Private AI Agents on Your Infrastructure
Qwen-AgentWorld is designed for teams that need to own their agent logic and data. LLM.co helps you deploy, fine-tune, and operate this model in your own environment—no vendor lock-in, full data control. Start building custom agentic workflows today.