Trillion-Parameter MoE Model Weights Released: A Practical Guide to Deployment and Agent Evaluation

1. Introduction: Trillion-Scale Open Models Reach Practical Maturity

Since 2024, the LLM field has seen a clear trend: MoE (Mixture of Experts) models with parameter counts beyond one trillion have begun releasing their complete weights. Academic and enterprise R&D teams are no longer limited to API calls — they can download the parameters, deploy them in-house, fine-tune them, and even run safety audits. Recently, a Chinese AI research team officially released the weights of a MoE model with roughly 2.8 trillion total parameters on a major model hosting platform, fulfilling an earlier promise to open the model within a month. At the same time, the model took first place among open-weight models on an evaluation based on real user agent sessions, sparking broad industry discussion about agent capability and the evolution of LLM architectures.

This article offers a comprehensive, neutral technical analysis of the event from five angles: model architecture, quantization and storage strategy, deployment hardware requirements, API access, and agent evaluation methodology.

2. Model Architecture: Sparse Attention Meets Deep MoE

2.1 Overall Structure

According to the official technical documentation and model card, the model uses a 93-layer Transformer, of which 69 layers adopt a new sparse attention mechanism (Gated DeepSeek Sparse Attention, or Gated DSA), while the remaining 24 layers keep the traditional Gated MLA (Multi-head Latent Attention) structure. This hybrid-layer design is deliberate: sparse attention layers model global dependencies in long-sequence scenarios at much lower computational cost, while the traditional MLA layers handle scenarios that demand fine-grained attention.

2.2 The Expert System

The core of the MoE architecture is expert routing. The model is configured with 896 expert networks, and each token activates only 16 of them during a forward pass, plus 2 shared experts that always participate in the computation. In numbers:

• Total parameters: about 2.78 trillion (2.78T), covering all expert weights.
• Activated parameters per token: about 104 billion (104B), far below the total, which keeps inference efficient.
• Routing policy: Top-16 selection plus shared experts as a baseline, preserving model capacity while controlling compute cost.

From an engineering perspective, a scale of 896 experts imposes steep requirements on memory management and expert loading strategy. At inference time, efficiently loading the weights of the 16 selected experts into GPU memory is one of the central deployment challenges.

2.3 Vision Encoder

The model ships with a separate vision encoder of roughly 401 million (401M) parameters, built on an improved vision Transformer architecture, supporting joint image-text input. This gives it multimodal understanding and the ability to process prompts that mix images and text.

3. Quantization and Storage: MXFP4 + Quantization-Aware Training

3.1 Mixed-Precision Quantization Scheme

At the weight-storage level, the model uses MXFP4 (4-bit floating point), while activation computations at inference run in MXFP8 (8-bit floating point). This “store at very low precision, compute at moderate precision” strategy is one of the mainstream approaches for deploying ultra-large models today:

Dimension	Precision format	Purpose
Weight storage	MXFP4	Dramatically reduces disk footprint and transfer bandwidth
Activation computation	MXFP8	Preserves inference accuracy and limits accumulated numerical error
Training method	Quantization-Aware Training (QAT)	Simulates quantization error during training to improve post-quantization accuracy

3.2 Storage Footprint Analysis

The model repository occupies about 1.56 TB on the hosting platform. With 2.78T parameters stored in MXFP4, the theoretical minimum size is:

2.78T × 4 bits / 8 = 1.39 TB

The actual 1.56 TB is slightly above the theoretical value because the repository also contains model configuration files, the tokenizer, vision module weights, and safetensors metadata overhead. For teams that need to download and self-deploy, a download of nearly 1.6 TB is a real test of network bandwidth and storage infrastructure.

3.3 Why Quantization-Aware Training Matters

Quantization-Aware Training (QAT) injects quantization noise during the training or fine-tuning stage, so the model weights gradually adapt to low-precision representation while learning. Compared with Post-Training Quantization (PTQ), QAT typically retains accuracy better at the same bit width — a critical advantage at extreme widths such as 4-bit.

4. Local Deployment: Hardware Requirements and Inference Framework Support

4.1 Estimating Hardware Requirements

Based on calculations from community engineers, one workable setup for running the model requires:

• GPU configuration: 8 enterprise-class, large-memory data center GPUs (on the order of ~2.3 TB of memory per card)
• Total memory demand: covering model weight loading, KV cache, and activations, estimated at more than 18 TB
• Hardware cost: roughly USD 550,000–650,000 (a third-party estimate, not an official recommendation)

It should be stressed that this estimate does not account for high-availability redundancy, network interconnect bandwidth, or storage I/O costs. For the vast majority of companies and research institutions, building a GPU cluster of this scale is not realistic; more teams will rely on cloud GPU instances or managed inference services instead.

4.2 Inference Framework Support

Mainstream LLM inference frameworks have already published deployment recipes for this model, including:

• Distributed inference engines supporting tensor parallelism and expert parallelism
• Frameworks with PagedAttention for efficient KV cache management
• Custom inference acceleration libraries for high-throughput scenarios

This framework support means developers can use mature toolchains to bring up a service quickly rather than building an inference pipeline from scratch. In practice, the typical workflow looks like this:

# Typical deployment workflow (pseudo-code)
1. Download the model weights to local high-speed storage
2. Load the model configuration with an inference framework
3. Configure tensor parallelism and expert parallelism strategy
4. Start the service and verify KV cache capacity
5. Expose the service through an API gateway

4.3 API Access

For teams without local deployment capacity, the model is also available through a cloud API. The API is compatible with the mainstream OpenAI format as well as another general-purpose interface format, supporting multi-turn conversation and tool calling. A few points are worth noting:

• The reasoning effort parameter offers three levels — low, high, and max — with max as the default; users can adjust it to match task complexity.
• In multi-turn tool calling, the client must send back the previous round’s reasoning content together with the tool call results to keep the context coherent.
• A high-speed endpoint is also offered; the vendor claims it runs 2–3× faster than the standard one, making it suitable for latency-sensitive scenarios.

5. Agent Evaluation: Measuring Capability from Real Sessions

5.1 Methodology

Unlike traditional static benchmarks, agent evaluation measures model capability in real, open-ended task environments. This evaluation system extracts multi-dimensional signals from real user agent sessions:

• Task completion confirmation: whether the user explicitly confirms the task was completed successfully
• Sentiment feedback: positive praise and negative complaints about the model’s output
• Correction response: whether the model adjusts correctly after the user corrects it
• Command execution success rate: whether tool calls such as shell commands execute successfully

These signals are combined into a Net Improvement metric; a positive value means the model outperforms the baseline on that dimension.

5.2 Reading the Results

In the evaluation published on the day of the weight release, based on more than 17,500 real long-horizon sessions, the model (at the max reasoning effort setting) achieved a net improvement of +5.1% — first among open-weight models and 13th on the overall leaderboard. By category:

• Work: first among open models
• Code: first among open models
• Chat: first among open models

For comparison, the previous-generation model scored a net improvement of only +0.91% under the same evaluation system, ranking sixth among open models. On a specific sub-metric, the model improved Bash command reliability by +7.9% (versus -4.5% for the previous generation), showing clear progress on code-execution agent tasks.

5.3 Takeaways for Developers

Agent capability evaluation is shifting from “answering questions” to “getting things done.” For developers, this means:

1. Model selection should not rely only on static metrics; pay closer attention to stability in real tool-calling scenarios.
2. Long-context capability (the model supports a 1M-token context) is the underlying infrastructure for agents executing complex, multi-step tasks.
3. Setting the reasoning effort parameter appropriately balances cost and quality.

6. Conclusion and Outlook

The release of trillion-parameter MoE model weights marks a shift in LLM technology from “black-box APIs” toward “controllable open source.” The combination of MXFP4 quantization and quantization-aware training makes local deployment of ultra-large models feasible at an acceptable accuracy cost, while evaluation systems built on real agent sessions provide a more credible measure of practical capability.

For enterprise developers, the key decisions ahead revolve around one question: build your own GPU cluster, use elastic cloud GPU instances, or consume the cloud API directly? Each path fits different scenarios, and understanding the model’s architecture, quantization strategy, and evaluation signals is the prerequisite for making an informed choice. As more trillion-scale models gradually open up, the engineering adoption of LLMs is set to enter a new phase of acceleration.