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    Deploying and Tuning a 2.8T MoE Model for Long-Horizon Agents

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    A practical guide to deploying and optimizing a 2.8T-parameter sparse-attention MoE model for long-context, multi-step agent workloads, covering architecture, benchmarks, training strategies, and engineering deployment.

    How Sparse Attention Supports Million-Token Contexts

    Traditional full attention creates exponential growth in memory and compute costs as context length reaches the million-token scale. This model uses a hybrid scheme across its 93 Transformer layers: 24 layers of Gated DSA plus 69 layers of Delta Attention. Gated DSA, or Dynamic Sparse Attention, dynamically selects the 2048 most relevant historical tokens for each attention computation, substantially reducing KV cache pressure and making native 1-million-token input feasible. The architecture also supports image-text alignment natively, removing the need for external OCR preprocessing and laying a foundation for terminal-operation and 3D-design agents.

    Designing Multi-Step Task Capabilities for Long-Horizon Agents

    The model is optimized specifically for code development, knowledge work, and related scenarios:

    • Terminal and code: It can understand and generate terminal commands, locate and fix bugs in large codebases, and convert natural language into structured modeling instructions through CAD tool APIs.
    • Multiple thinking budgets: The low budget suits simple Q&A and reduces latency. The high budget balances speed and depth, making it appropriate for routine coding and document summarization. The max budget targets complex mathematical reasoning, long-range code refactoring, or financial analysis requiring strict logical chains. Developers can configure the budget dynamically based on task complexity to achieve the best trade-off between compute and quality.

    How Joint Reinforcement Learning Avoids Capability Forgetting

    During post-training, the model adopts a joint reinforcement learning scheme covering hundreds of domains, including code, reasoning, tool calling, and mathematics. Multi-dimensional reward models prevent capability degradation caused by optimizing for a single task. The training process also introduces MXFP4 (4-bit floating point) quantization, meaningfully lowering deployment memory requirements while maintaining strong inference accuracy. Note that 4-bit quantized training places certain demands on stability, so precision validation is recommended for critical business scenarios in practice.

    Benchmark Results and Real-World Performance

    Official benchmarks with the default max thinking budget show: Terminal-Bench 4.0 at 65.7, SWE Marathon v1.1 at 51.7, DeepSWE v1.1 at 75.5, BrowseComp at 92.1, BenchCAD with tools at 92.7, and OfficeQA Pro v2 at 64.4.

    Testing conditions: The above results are based on 8x H100 80GB GPUs, batch size 1, the June 2025 official evaluation framework, and data from the project’s official technical report. Production performance varies depending on prompt engineering and business data.

    Engineering Deployment: Memory Requirements and Code Example

    Memory and Multi-GPU Configuration Reference

    GPU Count Total Memory (80GB/GPU) Estimated Usage Recommended Parallel Strategy
    4x H100 320 GB Insufficient, quantization required Not recommended
    8x H100 640 GB Near limit Tensor parallelism + MXFP4
    16x H100 1280 GB Sufficient Tensor parallelism + pipeline parallelism

    Multi-Round Tool Calling Code Example

    The following code is based on an OpenAI-compatible API served through vLLM. Ensure the service is running, authentication is configured, and requests is installed:

    1. import requests, json
    3. API_URL = "http://localhost:8000/v1/chat/completions"
    4. HEADERS = {"Content-Type": "application/json", "Authorization": "Bearer YOUR_API_KEY"}
    6. messages = [{"role": "user", "content": "Check today's weather in Beijing and determine if it is suitable for outdoor exercise."}]
    8. payload = {
    9.     "model": "large-moe-model",
    10.     "messages": messages,
    11.     "thinking_budget": "high",
    12.     "tools": [{
    13.         "type": "function",
    14.         "function": {"name": "get_weather", "parameters": {"city": "string"}}
    15.     }]
    16. }
    18. resp = requests.post(API_URL, json=payload, headers=HEADERS).json()
    19. if 'tool_calls' in resp['choices'][0]['message']:
    20.     tc = resp['choices'][0]['message']['tool_calls'][0]
    21.     messages.append(resp['choices'][0]['message'])
    22.     messages.append({
    23.         "role": "tool",
    24.         "tool_call_id": tc['id'],
    25.         "content": json.dumps({"city": "Beijing", "weather": "sunny", "temp": "23C"})
    26.     })
    27.     final = requests.post(API_URL, json={"model": "large-moe-model", "messages": messages}, headers=HEADERS)
    28.     print(final.json()['choices'][0]['message']['content'])

    Key Considerations

    • History management: In multi-round calls, preserve the complete thinking and tool interaction history. Truncation can lead to loops or off-target responses.
    • Security: API keys should have access control and regular rotation to prevent credential leakage and resource abuse. Sensitive data should be sanitized before being sent to the model.
    • License compliance: The model weights are not released under standard licenses such as Apache 2.0 or MIT. Enterprises must review the open-source license terms item by item before commercial use.

    Choosing a Cloud Platform for Enterprise Deployment

    When introducing such ultra-large MoE models, it is advisable to build an elastic compute scheduling system using mainstream cloud infrastructure services: use container platforms and elastic GPU clusters to absorb traffic peaks, wrap internal ERP, database, and other APIs as standard tools through serverless compute, and integrate logging and monitoring for end-to-end observability. Regardless of the cloud provider, the core principles are resource elasticity, security compliance, and cost control.

    FAQ

    Q: How should I choose the number of activated parameters for an MoE model?
    A: Larger activated parameter counts produce stronger single-pass inference but require more memory. The 104B activated configuration needs multi-GPU parallelism on current hardware. Balance it against task accuracy requirements.

    Q: What are the most common failure causes for long-horizon agents?
    A: Malformed tool-call results, loss of intermediate state, and truncation of overly long prompts are the three main causes. Pay close attention to message management and result validation.

    Q: How should I configure the thinking budget?
    A: Use low for simple Q&A, high for routine tasks, and max only when a strict reasoning chain is required. Avoid unnecessary compute waste.

    Conclusion

    The 2.8T MoE architecture combined with sparse attention provides a viable path to million-token inference. Multiple thinking budgets and joint reinforcement learning give the model strong planning and execution capabilities for specific long-horizon tasks, while human supervision and continuous tuning remain necessary. Understanding the architectural principles and mastering the engineering deployment details will be a key step for developers building the next generation of high-productivity AI applications.

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