Open Weights for 2.8T Parameter MoE Model: Deployment and Quantization Practices

1. Background: Why This Weight Release Matters

Recently, a Mixture-of-Experts (MoE) model with a total parameter scale of approximately 2.8 trillion officially released its complete weights. Unlike previous releases that only offered API access or partial weights, this release allows development teams to directly access the model files for local deployment, weight auditing, continuous training, or domain-specific fine-tuning.

From an industry perspective, this marks a shift for ultra-large-scale sparse models from “cloud-only API access” to a phase of “weight availability and verifiable deployment.” For enterprise developers and research teams, this progress extends beyond raw model capability. It introduces a series of engineering challenges, including model architecture understanding, quantization format adaptation, inference framework selection, and hardware cost estimation.

Official information indicates that the model supports a context window of approximately 1 million tokens and can process multimodal (image and text) inputs. In agent evaluations, it ranked first among open-weight models. Compared to previous versions, significant adjustments were made to its attention layers and sparse attention mechanisms, aiming to improve long-context modeling efficiency and reduce inference costs.

2. Model Architecture: Key Adjustments from Attention to Expert Layers

Understanding an ultra-large MoE model requires breaking down its structural components. According to public information, the model has 93 layers: 69 layers utilize a KDA structure, while 24 layers use a Gated DSA (Gated Deep Sparse Attention) structure.

A key change from the previous generation is the replacement of full attention layers with gated sparse attention layers. The primary benefit of this replacement is computational efficiency for long contexts. In traditional dense attention, an increase in sequence length results in a roughly quadratic growth in computation. Under the sparse attention mechanism, computational consumption for long sequence modeling is reduced through selective focus. For scenarios involving million-token contexts, this design choice offers direct engineering value.

Regarding the expert layers, the model contains 896 experts, activating 16 experts per token, alongside 2 shared experts. The vision module employs a vision encoder with approximately 401 million parameters. It is crucial to note the significant difference between the “2.8T total parameters” and the “104B activated parameters per token.” This is the core characteristic of the MoE architecture: a massive amount of parameters is stored in the expert layers, but only a fraction is activated during a single forward pass.

This sparse activation design directly impacts deployment: storage requirements depend on the total parameter count, while inference latency and computational demands depend largely on the number of activated parameters. Therefore, when evaluating hardware, one must consider both the VRAM capacity required for weight storage and the computational throughput needed during inference.

3. Weight Format and Quantization Strategy

Weight storage utilizes the MXFP4 format, while activations use the MXFP8 format. Compared to traditional FP16 or BF16, this mixed-precision scheme significantly reduces the storage footprint of weight files. Public information shows that the model repository occupies approximately 1.56TB of space, with the parameter count derived from weight files totaling around 2.78 trillion.

Official sources confirm the use of Quantization-Aware Training (QAT), which is a critical detail. The fundamental difference between QAT and Post-Training Quantization (PTQ) is that QAT simulates quantization errors during the training phase, allowing model parameters to maintain better output quality even at low-precision representations. For an ultra-large model with 4-bit weights, relying solely on PTQ could lead to a significant degradation in capability due to the accumulation of errors across multiple layers. QAT largely mitigates this issue.

From a development perspective:

• MXFP4 Weight Storage: Each weight uses an average of about 4 bits for storage, offering roughly a 4x compression ratio compared to BF16.
• MXFP8 Activations: During inference, activation values use an 8-bit dynamic range, balancing precision and computational efficiency.
• Microscalar Formats: MX formats typically employ block-wise shared exponents, maintaining a more stable numerical range at low bit-widths.

For teams intending to load this model, it is recommended to first verify whether the current inference framework supports the corresponding MX formats. If the framework does not natively support them, directly loading the weight files may fail or result in precision anomalies.

4. Deployment Tools and Hardware Thresholds

Mainstream distributed inference frameworks provide deployment configurations for this model. These configurations typically cover key aspects such as weight sharding, expert parallelism, tensor parallelism, and KV cache management.

From a hardware perspective, the deployment threshold is notably high. Based on community estimates, a viable configuration involves a single machine equipped with 8 high-performance computing cards, providing a total of about 2.3TB of VRAM. The hardware procurement cost for this setup is estimated to be between $550,000 and $650,000. This is a third-party estimate rather than an official recommendation, but it serves as a useful reference.

The method for calculating VRAM requirements can be simplified as follows:

• Weight Storage: Approximately 1.56TB.
• Activations and KV Cache: Depends on batch size, sequence length, and parallelization strategy.
• Framework and Runtime Overhead: An additional portion of VRAM should be reserved.

For most small to medium-sized teams, the economic viability of purchasing specialized hardware for local deployment needs careful evaluation. If the business scenario has strict requirements for data privacy or model controllability, local deployment makes sense; otherwise, accessing the model via API is a more practical path.

5. API Integration and Inference Parameters

If local deployment is not pursued, the model can be accessed via its API. The interface is compatible with common chat completion protocols, facilitating a quick transition for existing applications. The official API offers two options: a standard version and a high-speed version. The high-speed version boasts inference speeds 2 to 3 times faster than the standard version, making it suitable for latency-sensitive scenarios.

Regarding inference parameters, reasoning effort is categorized into three levels: low, high, and max, with “max” as the default. Developers integrating this API must understand the semantics of this parameter: a higher reasoning effort typically corresponds to a longer chain-of-thought output, which may yield better results on complex tasks. However, it also increases the time to the first token and the total number of output tokens, directly driving up API usage costs.

Multi-turn tool calling is another easily overlooked detail. When the model generates reasoning content during a tool calling scenario, the subsequent request must pass back both the previous reasoning content and the tool call information. Omitting this may cause the model to lose contextual intent or repeat tool calls. In actual development, this requirement must be incorporated into the client-side state management logic.

6. Practical Recommendations

For teams planning secondary development based on these weights, the following path is recommended:

1. Validate Capability Boundaries via API First: Before investing in hardware costs, run the evaluation process for your target scenarios using the API to confirm the model’s capabilities meet your needs.
2. Evaluate the Necessity of Local Deployment: Make a comprehensive judgment based on data compliance requirements, cost sensitivity, and customization needs.
3. Proceed with Local Deployment: If decided, prioritize inference frameworks that already offer deployment configurations, and validate using the parallelization sharding schemes provided officially.
4. Verify Quantization Format Compatibility: Before loading weights, confirm the inference framework’s support for MXFP4/MXFP8 to avoid precision loss.
5. Design Tool Calling State Management: Explicitly save and pass back the reasoning context and tool call chain on the client side to ensure consistency across multi-turn interactions.

Overall, the release of the 2.8T parameter MoE model weights provides a comprehensive reference sample for the engineering deployment of ultra-large-scale sparse models. For developers, the focus should extend beyond the model’s capabilities to the engineering tradeoffs made in its architectural design, quantization scheme, and inference framework adaptation.