Ultra-Large MoE Model Weights Released: Architecture Analysis and Local Deployment Guide

A leading large model research team has fulfilled its open-source commitment by releasing the full weights of its latest ultra-large Mixture-of-Experts (MoE) model. The model ranks first among open-weight models on a prominent agent capability benchmark and also places near the top of the overall leaderboard. This release provides the open-source community with a powerful foundation model resource and gives enterprise developers a basis for deep auditing, fine-tuning, and private deployment.

This article sets aside brand-specific details and offers an objective technical analysis of the model’s underlying architecture innovations, quantized storage mechanisms, local deployment requirements, and API integration best practices, serving as a detailed technical reference for teams considering adopting an ultra-large model.

1. Core Architecture: Sparse Design and Long-Context Modeling

The model has a total parameter count of 2.78 trillion (~2.8T), making it a classic ultra-large MoE architecture. To control inference costs while maintaining model capability, it employs a highly sparse activation mechanism: each token activates only about 104B parameters. Below are the key architectural design choices.

1.1 Hybrid Attention Layer Design

The model contains 93 layers in total. Unlike a conventional single attention mechanism, the architecture mixes two different layer types:

• KDA Layers (69 layers): These form the main body of the model, handling standard semantic feature extraction and representation.
• Gated Sparse Attention Layers (24 layers): This is the core upgrade over the previous generation. The prior architecture typically used full attention mechanisms (such as gated multi-head latent attention), where compute and memory overhead for long-context modeling grow quadratically. This model introduces gated deep sparse attention, which substantially reduces resource consumption for long-context modeling by sparsifying query-key interactions while preserving the ability to capture critical information.

1.2 Expert Network and Routing Mechanism

The expert layers consist of 896 independent experts. During each forward pass, a gated routing network dynamically selects the 16 best-matching experts for each token. In addition, 2 shared experts are retained to handle foundational general-purpose features needed by all tokens. This “shared + exclusive” hybrid expert mechanism effectively improves multi-task generalization and computational efficiency.

1.3 Native Multimodal Fusion

The model supports not only text processing but also native image-text input, enabling it to handle multimodal contexts that include images. Its vision component consists of a ~401M-parameter vision encoder responsible for converting images into feature sequences of the same dimensionality as text. It supports context input of up to 1 million tokens, laying the groundwork for long-document understanding, multi-image cross-page reasoning, and complex codebase analysis.

2. Quantization-Aware Training (QAT) and Storage Mechanism

The primary challenge for ultra-large models is weight storage and transfer. Rather than using traditional FP16 or BF16 formats, this model adopts low-bit quantization techniques at its core.

• Weight Storage (MXFP4): Model weights are stored in a 4-bit Microscaling FP4 format. Compared with traditional 16-bit storage, this directly compresses the model repository’s disk footprint to approximately 1.56TB.
• Activation Computation (MXFP8): During inference, activation values are processed in an 8-bit microscaling floating-point format. Compared with integer quantization, floating-point quantization offers better numerical expressiveness when handling long-tail activation distributions.
• Quantization-Aware Training (QAT): The team emphasizes that these low-bit formats are not the result of simple post-training quantization (PTQ). Instead, quantization noise was introduced during the training process itself. This means the model was trained to “adapt” to low-precision numerical representation, maintaining near-lossless inference accuracy and stability even at extremely low bit rates.

3. Local Deployment Hardware Assessment and Inference Framework Compatibility

For enterprises seeking private deployment, the barrier to entry for an ultra-large MoE model is extremely high. Even with 4-bit weight quantization, loading a model file of nearly 1.56TB still places stringent demands on VRAM capacity.

3.1 Hardware Resource Estimation

According to common industry VRAM calculation logic, beyond the model weights themselves, the inference process also requires allocation for KV Cache, activation values, and context buffers. For example, to support a 1-million-token context, a single-machine deployment typically needs to reserve over 2.3TB of VRAM.

• Reference Configuration: Third-party developers estimate that a viable single-machine configuration would be equipped with 8 high-performance compute accelerator cards (such as the latest B-series architecture, with each card having a large VRAM capacity), with total server VRAM reaching approximately 2.3TB. Hardware costs for servers at this tier typically range from $550,000 to $650,000. This indicates that full local deployment of such a model currently remains feasible only for leading enterprises.

3.2 Mainstream Inference Engine Compatibility

To facilitate rapid adoption, the model release includes deployment recipes for several mainstream open-source inference frameworks:

• High-Performance Inference Engine A (name substituted): Focused on high throughput and continuous batching, suitable for serving high-concurrency inference as an API backend.
• Long-Context Optimization Engine B (name substituted): Features deep kernel optimizations for handling ultra-long contexts (e.g., 1 million tokens) and supports more efficient KV Cache management, suitable for long-document processing scenarios.
• Domestic Acceleration Engine C (name substituted): Optimized for a specific domestic hardware ecosystem, offering a more localized deployment experience.

During deployment, developers only need to pull the corresponding safetensors weight files and load them using the configuration files provided by each engine. Note that because the model uses MXFP4/MXFP8 formats, before deployment you must ensure that the inference engine and underlying hardware drivers support the relevant low-bit compute instruction sets.

4. API Integration and Multi-Turn Tool-Calling Best Practices

If an enterprise lacks the hardware for local deployment, calling the model via cloud API is the most cost-effective approach. The model maintains strong compatibility at the API level and offers some unique advanced features.

4.1 API Interface Specifications and Reasoning Depth Control

The API is fully compatible with standard conversational formats (such as request body structures compatible with OpenAI and Anthropic), allowing developers to seamlessly migrate existing code. Available model names include a standard version and a high-speed version (approximately 2-3x the speed of the standard version).

A key advanced parameter is reasoning_effort, which controls the depth of the model’s internal thinking before generating a response. This parameter supports three options: low, high, and max, with a default of max.

• low: Suitable for simple Q&A, information retrieval, and other scenarios with strict real-time requirements, reducing internal reasoning token generation.
• high/max: Suitable for complex code generation, deep logical reasoning, mathematical computation, and similar scenarios, where the model invests more resources in chain-of-thought construction.

4.2 Agent Tool-Calling State Management

As a model that performs well on Agent Arena, its multi-turn tool-calling mechanism is a key area for developers to understand. In multi-turn conversations, after the model decides to call an external tool (such as executing a Shell command or querying a database), when passing the tool execution result back to the model, you must include the reasoning_content (reasoning content) and tool_calls (tool call requests) returned by the model in the previous turn.

This is critical: it allows the model, upon receiving the tool execution result, to review its prior reasoning logic and calling intent, thereby making more coherent and accurate next-step decisions. If only the tool result is passed back while discarding the reasoning context, the model may experience logical discontinuity or repeatedly call the same tool.

Below is a simplified request example structure for multi-turn tool calling:

{
  "model": "large-moe-model-highspeed",
  "messages": [
    {"role": "user", "content": "Help me check today's weather in Beijing"},
    {"role": "assistant", "content": null, "reasoning_content": "The user needs weather information. I need to call the weather API.", "tool_calls": [{"id": "call_001", "type": "function", "function": {"name": "get_weather", "arguments": "{\"city\": \"Beijing\"}"}}]},
    {"role": "tool", "tool_call_id": "call_001", "content": "{\"temp\": 25, \"condition\": \"sunny\"}"}
  ],
  "reasoning_effort": "max"
}

5. Agent Capability Benchmark Signal Analysis

The model’s ranking as the top open model on a prominent Agent benchmark stems from its strong tool-calling reliability and long-horizon task planning capabilities. Unlike traditional static benchmarks, the leaderboard data is derived entirely from real user Agent sessions.

The evaluation system extracts four core signal categories to calculate a “net improvement” score:

1. Task success signal: The user explicitly confirms in the session that the task was completed successfully.
2. Sentiment feedback signal: The system uses natural language processing to analyze user praise or complaints about the model.
3. Error correction response signal: After the user points out a model error, whether the model can correctly rectify it and provide a reasonable response in subsequent interactions.
4. Execution reliability signal: Particularly in code and automation tasks, whether Shell commands or other executable code generated by the model run successfully without errors in practice.

Based on statistics from over 17,500 real long-horizon sessions, the model achieved up to +7.9% net improvement on sub-items such as Bash reliability, with an overall leaderboard net improvement of +5.1%, indicating that its engineering usability in real-world business scenarios has reached an industry-leading level.

Conclusion

The full release of this 2.8T-parameter MoE model’s weights marks a new phase for open-source large models in terms of parameter scale, architectural innovation, and engineering usability. From the combination of advanced deep sparse attention and quantization-aware training to its outstanding performance in real-world agent evaluations, it sets a new technical benchmark for the industry. For developers, deeply understanding its architectural principles and calling conventions, and reasonably assessing deployment costs versus business benefits, will be key to capturing the next wave of AI engineering opportunities.