Open-Sourcing 10K-Scale MoE Model Weights: Architecture and Deployment Practices

As large language models evolve towards ultra-large scale parameters, the Mixture of Experts (MoE) architecture has become the mainstream choice for balancing computational costs and model performance. Recently, an open-source large model officially released its complete weights, accompanied by a model card disclosing architectural parameters, evaluation results, and licensing. With a total parameter count of 2.8 trillion and 104 billion activated parameters, the model ranks first among open-weight models. This article strips away brand-specific attributes to deeply analyze the general technical practices for models at this ultra-large scale, covering the underlying architecture, quantized storage, capability evolution, and engineering deployment.

1. Ultra-Large Scale MoE Architecture Design: Sparse Activation and Long Context

With a total parameter count reaching 2.8 trillion, adopting a traditional dense model would make the computational overhead for inference and training unmanageable. Therefore, this model continues to utilize the mature MoE underlying architecture design.

1. Sparse Activation Mechanism
The core of the MoE architecture lies in “sparse activation.” Out of the 2.8 trillion total parameters, only 104 billion are dynamically activated during a single inference pass. This means that for any given input token, the routing network assigns it to the most matching small subset of expert sub-networks for computation based on its features. This mechanism controls the actual computational consumption of a single inference to the tens of billions level, significantly reducing inference latency while preserving the high capacity and generalization ability of a trillion-level model.

2. Million-Token Context Window
The model supports a context window of 1 million tokens. The core challenge in processing ultra-long contexts is that the computational complexity of the attention mechanism grows quadratically with sequence length. In such architectures, variants of Rotary Position Embedding (RoPE) or underlying memory optimization technologies like Ring Attention and FlashAttention are typically introduced. These ensure that under million-token input, both memory usage and computational overhead remain within a linearly controllable range acceptable to the hardware.

2. Storage Optimization: MXFP4 Low-Precision Quantization Practices

Another major engineering challenge for trillion-parameter models is weight storage. Storing weights in the original 16-bit floating-point format places extreme demands on the VRAM of compute clusters, making low-precision quantization the key breakthrough.

1. Extreme Compression of Storage Volume
Taking the 2.8 trillion parameter model as an example, storing it in traditional 16-bit precision (FP16/BF16) would result in a model weight file size of approximately 5.6TB. This is unmanageable for a single 8-GPU server (even with 80GB VRAM per GPU, the total machine memory is only 640GB). By introducing the MXFP4 low-precision quantization scheme, the model size is successfully compressed to 1.56TB. This allows models that previously required tensor parallelism across multiple machines to potentially be loaded within a more compact cluster topology.

2. Balancing Quantization Precision and Performance
MXFP4 utilizes a 4-bit floating-point representation. Compared to traditional INT4 integer quantization, the floating-point format better preserves outlier distributions, thereby keeping precision loss within an acceptable range while extremely compressing the model size. Before actual deployment, developers typically need to conduct rigorous perplexity evaluations to ensure that the quantized model does not exhibit significant degradation in complex logical reasoning and code generation.

3. Post-Training Reinforcement Learning: The Core Engine for Capability Leaps

Compared to the pre-training of the base model, the significant capability improvement in this version is primarily attributed to the increased focus on reinforcement learning during the post-training phase.

1. Reinforcement Learning Application Strategies
In the post-training phase, the model optimizes its policy through massive reward signals. Common methods include Reinforcement Learning from Human Feedback (RLHF) or Direct Preference Optimization (DPO). By scaling up reinforcement learning, the model achieved stronger generalization capabilities in instruction following, multi-turn dialogue coherence, and complex task decomposition.

2. Real-World Scenario Driven Evaluation Validation
The model’s capability improvements were validated in long-cycle agentic sessions with real users. Based on over 17,500 real evaluation data points, this version of the model ranked first among open models in three core scenarios: work, coding, and chat. Its net improvement rate reached +5.1%, far exceeding the +0.9% of the previous generation version. This indicates that the optimization strategy in the post-training phase successfully reached the model’s capability bottleneck, achieving a substantial breakthrough rather than mere parameter stacking.

4. Enterprise-Grade Deployment and Scheduling Architecture Guide

Faced with 1.56TB of quantized weights and a million-token context, enterprises need to build a highly available scheduling and deployment architecture for actual implementation. The following is a general engineering practice reference:

1. Weight Distribution and Storage Layer Design
Distributing a 1.56TB model file during multi-node distributed deployment will face severe network bandwidth bottlenecks. It is recommended to use object storage as the unified storage pool for model weights. During instance scaling, pull the weights to local nodes using a high-speed intranet.
If deploying in a cloud-native environment, message queues can be integrated to implement asynchronous notifications for model loading status. This ensures that multi-GPU inference processes only start the inference engine after the weights are fully ready, avoiding Out of Memory (OOM) errors caused by uneven memory allocation.

2. Inference Engine and VRAM Scheduling
For the MoE architecture, the choice of inference engine is critical. Below is a pseudo-code deployment workflow example based on a general container platform:

# Container orchestration configuration example (general structure)
apiVersion: apps/v1
kind: Deployment
metadata:
  name: moe-inference-engine
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: inference-server
        image: registry.example.com/moe-server:latest
        resources:
          limits:
            nvidia.com/gpu: 8  # Single node 8-GPU driver
        env:
        - name: MODEL_PATH
          value: "/mnt/storage/moe-mxfp4-1.56tb"
        - name: TENSOR_PARALLEL_SIZE
          value: "8"
        - name: CONTEXT_WINDOW
          value: "1048576"
        volumeMounts:
        - name: model-volume
          mountPath: /mnt/storage
      volumes:
      - name: model-volume
        persistentVolumeClaim:
          claimName: moe-model-pvc

3. Long Context KV Cache Management
During 1 million token inference, the VRAM growth of the KV Cache is the primary bottleneck. In addition to relying on the PagedAttention technology of the underlying framework, it is recommended to configure dynamic batching and session truncation strategies at the gateway layer. By monitoring the token length of individual sessions in real-time through logging services, an automatic summarization and compression mechanism is triggered when a preset threshold is exceeded, thereby safeguarding the overall throughput of the service.

5. Engineering Adaptation for Agentic Scenarios

From the benchmark evaluation data, long-cycle agentic sessions are the core litmus test for measuring the capabilities of such ultra-large models. In agentic scenarios, the model needs to call external tools, maintain long-term memory, and perform self-reflection.

When building such application architectures, developers should focus on:

• Tool Calling Format Alignment: Ensure that the model’s tool calling format (e.g., JSON Schema) from the post-training phase is compatible with the enterprise’s existing API gateway standards.
• External Memory Mechanism: Combine the million-token window with a vector database. Long-cycle historical information should go through semantic retrieval, while short-term multi-turn interactions should use the model’s native context, building a hybrid memory system.

Conclusion

The open-sourcing of model weights with 2.8 trillion total parameters and 104 billion activated parameters marks the official entry of open-source large models into the ultra-large scale MoE era. Compressing storage to 1.56TB through MXFP4 quantization technology, combined with the capability leaps brought by post-training reinforcement learning, provides the industry with a highly valuable underlying foundation. For enterprise developers, understanding its sparse architecture characteristics and rationally building distributed storage and inference scheduling systems will be the key to harnessing such ultra-large models and unlocking their agentic potential.