Trillion-Parameter MoE Model Weights Released: Deployment Tech for Ultra-Long Context Agents

1. Background: Engineering Significance of Open Weights

In early November, an AI research team officially released the complete weights of its flagship MoE model to a public model hosting platform, fulfilling its promise to open-source weights within two weeks of launch. For teams needing to deploy, fine-tune, or audit models independently, this transition moves them from being restricted to API calls to having full control over the inference stack.

The core specifications of the model are as follows:

• Total Parameters: ~2.78 trillion (safetensors statistics)
• Activated Parameters per Token: 104B
• Architecture Type: MoE (Mixture of Experts)
• Context Length: 1 million tokens
• Input Modalities: Text + Image
• Weight Storage Format: MXFP4
• Activation Precision: MXFP8
• Training Method: Quantization-Aware Training (QAT)

From an engineering perspective, the key value of this release is not merely “another available model,” but that it validates a specific path: ultra-large-scale MoE models can utilize low-bit storage for weights while maintaining usable precision under a QAT framework. This provides direct reference value for reducing VRAM usage and bandwidth requirements on the inference side.

2. Model Architecture: Trade-offs in Sparse Attention

2.1 Layer Structure and Attention Mechanism

According to the model card, the model comprises 93 layers, of which:

• 69 layers use KDA (Kernel Density Attention)
• 24 layers use Gated DSA (Gated DeepSeek Sparse Attention)

Compared to its predecessor, the most significant architectural change is replacing full-attention Gated MLA layers with Gated DSA. The engineering motivation for this replacement is clear: in a 1 million token context scenario, the computational complexity and KV Cache footprint of full attention expand drastically. Sparse attention mitigates this by computing attention weights only between selected token pairs, significantly reducing computational overhead while preserving long-range modeling capabilities.

The Gated mechanism introduces learnable gating signals, enabling the model to dynamically decide which attention paths require activation. This combination of “sparsity + gating” fundamentally seeks a Pareto optimum between expressiveness and inference cost.

2.2 Expert Layer Design

The design parameters for the MoE component are:

• Total Experts: 896
• Activated Experts per Token: 16
• Shared Experts: 2

The sparse activation ratio of 16 out of 896 is approximately 1.8%. This means that while the total parameter count reaches 2.8T, the parameters actively involved in computing each token are only about 104B. The inclusion of shared experts ensures the stability of base capabilities—regardless of the routing outcome, shared experts always participate in the computation, preventing drastic output quality fluctuations caused by routing instability.

2.3 Vision Encoder

The vision component utilizes MoonViT-V2, containing 401M parameters, to support mixed text and image inputs. For agent applications requiring document understanding, chart analysis, or UI screenshot parsing, this module serves as the foundation for multimodal capabilities.

3. Deployment Practices: Hardware Requirements and Inference Frameworks

3.1 VRAM Estimation

Weights are stored in MXFP4, occupying approximately 1.56TB. However, actual deployment VRAM requirements extend well beyond the weights themselves:

• Weight Loading: ~1.56TB (MXFP4)
• KV Cache: Even with sparse attention, KV Cache allocation for a 1 million token context can still reach hundreds of GBs.
• Activations and Intermediate Tensors: Correlate positively with batch size and sequence length.

Third-party estimates suggest a viable configuration: a server equipped with 8 high-performance GPUs totaling approximately 2.3TB of VRAM, costing roughly $550,000 to $650,000. It must be emphasized that these are third-party estimates; the official team has not provided recommended hardware configurations. Actual deployments must also account for:

• Tensor parallelism and pipeline parallelism partitioning strategies.
• Communication overhead of expert parallelism.
• Hardware support levels for quantization formats.

3.2 Inference Framework Support

Currently, mainstream inference frameworks have provided deployment solutions:

• vLLM: Supports PagedAttention and continuous batching, suitable for high-throughput scenarios.
• SGLang: Provides RadixAttention and structured generation optimizations, suitable for multi-turn agent calls.
• TokenSpeed: An optimization solution tailored for long-sequence scenarios.

Using vLLM as an example, a typical deployment command looks like this (parameters must be adjusted based on hardware):

# Illustrative command, not directly executable
vllm serve <model-path> \
  --tensor-parallel-size 8 \
  --max-model-len 1000000 \
  --quantization mxfp4 \
  --enable-expert-parallel \
  --gpu-memory-utilization 0.95

During actual deployment, note that loading MXFP4 weights requires framework-level support for this quantization format; expert parallelism requires handling all-to-all communication; and in long-context scenarios, close attention must be paid to KV Cache memory management strategies.

4. API Integration: From Calls to Multi-Turn Tool Orchestration

For teams preferring not to self-deploy, the API offers a lower barrier to entry. The model provides two variants: Standard and Fast, with the latter being approximately 2 to 3 times faster. The interface is compatible with mainstream conversational formats, supporting both OpenAI and Anthropic API styles.

4.1 Reasoning Effort Control

The API provides a reasoning_effort parameter with options low, high, and max (default is max). This parameter directly dictates the computation allocated during the reasoning phase:

• low: Suitable for simple Q&A, information extraction, and tasks requiring the fastest response.
• high: Suitable for complex problems requiring multi-step reasoning.
• max: Suitable for mathematical proofs, code generation, and in-depth analysis.

4.2 Critical Details for Multi-Turn Tool Calling

In agent scenarios, multi-turn tool calling is a core capability. Here is an easily overlooked yet crucial engineering detail: when conducting multi-turn tool calls, the previous round’s reasoning_content and tool_calls must be passed back together.

The reason is that the model’s reasoning process and tool-calling decisions are sequential and coherent. If only the tool execution result is returned and the previous reasoning content is discarded, the model loses the decision context, causing subsequent calling logic to break.

Illustrative call structure:

{
  "messages": [
    {"role": "user", "content": "Check today's weather in Beijing and recommend an outfit"},
    {"role": "assistant", "content": null,
     "reasoning_content": "Need to get weather data first...",
     "tool_calls": [{"id": "call_1", "function": {"name": "get_weather", "arguments": "{\"city\":\"Beijing\"}"}}]},
    {"role": "tool", "tool_call_id": "call_1", "content": "Sunny, 15-25C"},
    {"role": "assistant", "content": null,
     "reasoning_content": "Temperature is moderate, suggest a light jacket...",
     "tool_calls": [{"id": "call_2", "function": {"name": "recommend_outfit", "arguments": "{\"temp\":\"15-25\",\"condition\":\"Sunny\"}"}}]}
  ]
}

This design imposes specific requirements on agent orchestration frameworks: the state management module must fully retain the reasoning content and tool call records for every turn, rather than only retaining the final text output.

5. Agent Capability Evaluation: Extracting Signals from Real Sessions

On the day of the weight release, an evaluation platform published the model’s performance data in agent scenarios. Unlike traditional static benchmarks, this evaluation sourced data from real user agent sessions, calculating “net improvement” based on the following signals:

• Whether the user explicitly confirmed task success.
• Praises or complaints directed at the model.
• Response quality following user corrections.
• Whether shell commands executed successfully.

Statistical results based on over 17,500 real long-horizon sessions:

• This model (Max mode) achieved a net improvement of +5.1%.
• Ranked 1st among open-weight models.
• Ranked 13th overall.
• Ranked 1st among open-weight models in Work, Code, and Chat categories.
• The previous generation model had a net improvement of +0.91%, ranking 6th among open-weight models.

In the breakdown, Bash reliability performance was particularly outstanding: +7.9%, ranking 1st among open-weight models, compared to the previous generation’s -4.5% and 13th place. This metric directly reflects the model’s stability when executing commands in real terminal environments, serving as a valuable reference for teams requiring automated operations, CI/CD integration, and script orchestration.

6. Engineering Selection Recommendations

For teams currently evaluating whether to adopt this model, decisions should be made across the following dimensions:

Local Deployment Scenarios:

• High data compliance requirements prohibiting external API calls.
• Need for deep fine-tuning or model auditing.
• Long-term, large-scale inference demands where API costs exceed hardware amortization.

API Call Scenarios:

• Rapid product feasibility verification.
• Fluctuating inference demands requiring elastic scaling.
• Teams lacking operational experience with large-scale GPU clusters.

Key Risk Warnings:

• The hardware cost and operational complexity of local deployment are high and require thorough assessment.
• Framework support for quantization formats is still evolving; inference precision needs verification.
• KV Cache management in long-context scenarios remains an engineering challenge.

Overall, the release of weights for trillion-scale MoE models signifies that the large model ecosystem is evolving from “API black boxes” to “controllable inference stacks.” For teams with engineering capabilities, this enables deep optimization tailored to specific business characteristics. For application-layer developers, the API continues to offer a low-barrier integration path. Choosing the appropriate deployment modality requires a comprehensive consideration of data sensitivity, cost structure, team capabilities, and business growth expectations.