Large-Scale MoE Model Weight Release and Local Deployment Practices

1. Why This Weight Release Matters

In the evolution of large models, developers typically only get access to APIs, chat interfaces, or smaller open-source versions. Model weights are usually considered core assets. When a model reaches the trillion-parameter scale, releasing the weights allows external teams to deploy, fine-tune, prune, audit, or conduct interpretability research independently.

The recently released model has approximately 2.78 trillion total parameters and utilizes a sparse Mixture of Experts (MoE) architecture. During a single forward pass, each token does not activate all parameters; instead, it only activates a subset of expert paths. Consequently, there is a significant difference between its “total parameters” and “activated parameters per token.” According to public information, the model activates around 104 billion parameters per token. This design allows the model to perform single-pass inference with limited compute power, although the complete weights still require substantial storage and loading capacity.

For developers, the release of these weights means three things:

1. Offline deployment on proprietary hardware, ensuring data remains within the local domain.
2. Continued training or parameter-efficient fine-tuning for specific tasks.
3. Visibility into implementation details such as model structure, layer configurations, expert count, and quantization methods, enabling inference framework optimization.

2. Model Structure and Key Architectural Choices

2.1 Overall Size and Layer Distribution

According to the model card, the model comprises 93 layers. Among these, 69 layers use a structure referred to as KDA, while the remaining 24 use Gated DSA. While these abbreviations are not universal industry standards, their technical meaning can be inferred from the context: KDA points to an attention variant with key-value compression, and Gated DSA is closer to gated sparse attention.

Compared to the previous generation, a notable change is that some layers previously using full attention mechanisms have been replaced with gated sparse attention layers. The goal is clear: in ultra-long context scenarios, full attention incurs quadratic compute growth. Sparse attention reduces the cost of long-sequence modeling within a tolerable precision loss.

2.2 Expert Layer Design

The model’s expert layer contains 896 experts. For each token, 16 experts are selected during computation, along with 2 shared experts. The shared experts handle general semantic information, preventing all knowledge from being fragmented across routed experts and mitigating expert training imbalance.

For deployment teams, the number of experts directly impacts three factors:

• The number of weight file shards;
• Expert routing overhead during inference;
• The resident strategy for expert weights in VRAM.

896 experts is not an extreme number. Compared to dense models in the tens of billions parameter range, this sparse configuration can increase the model’s knowledge capacity by an order of magnitude while keeping the per-token compute volume roughly the same.

2.3 Visual and Multimodal Capabilities

In addition to the text path, the model includes a vision encoder with approximately 401 million parameters for processing image inputs. The standard approach for integrating vision modules into large language models is to split images into patches, convert them into token sequences via the vision encoder, and feed them into the main network alongside text tokens.

For multimodal deployment, the presence of a vision encoder means the inference framework must correctly handle image preprocessing, vision token injection, and cross-modal positional encoding. If deployers only need text capabilities, they can selectively ignore the vision path, though the weight file still contains these parameters.

3. Quantization Strategy: Different Handling for Storage and Activation

3.1 Why Differentiate Quantization for Storage and Activation

The model weights are stored in MXFP4 format, while activations use MXFP8 format. This distinction is not arbitrary but based on actual bottlenecks during inference.

Weight characteristics:

• Massive volume;
• Frequently read;
• Relatively static during inference;
• Greatest impact on VRAM usage.

Activation characteristics:

• Values change with every token;
• More sensitive to numerical precision;
• Noticeable degradation in model output quality if over-quantized.

Therefore, adopting a mixed-precision strategy of “4-bit weights, 8-bit activations” is a common approach in deploying large models today. It strikes a balance between VRAM capacity and inference precision.

3.2 The Significance of Quantization-Aware Training

Applying post-training quantization directly after training often introduces noticeable errors in certain layers. The official notes indicate that this model employs Quantization-Aware Training (QAT), meaning the model adapts to low-bit numerical representations during the training phase, thereby reducing precision loss during final deployment.

QAT is not a panacea, but it significantly mitigates the following issues:

• Distribution shifts in attention logits;
• Accumulated errors during forward propagation;
• Truncation errors caused by large activation values.

For teams handling their own deployment, if subsequent continued training or fine-tuning is required, it is not recommended to perform full-parameter training directly on 4-bit weights. A safer approach is to load the original or 8-bit precision weights, complete the training, and then apply deployment quantization.

4. Weight Distribution and Storage Scale

4.1 Repository Size: ~1.56TB

Based on information from the model hosting platform, the entire weight repository occupies approximately 1.56TB. This figure includes the main model weights, configuration files, and potential index files. Comparing this to the parameter scale provides a rough estimate of the storage cost per billion parameters.

For average developers, 1.56TB is not a “download and try” scale. It requires the downloading environment to have:

• Sufficient disk space, preferably using NVMe or high-speed object storage;
• Stable network bandwidth;
• Download tools supporting resumable transfers;
• Adequate time for verification to ensure shard file integrity.

4.2 Inference Framework Adaptation

Public information indicates that multiple mainstream inference frameworks now provide deployment solutions for this model. While specific tools are not named here, deploying trillion-level MoE models typically relies on the following capabilities:

• Expert parallelism;
• Tensor parallelism;
• Pipeline parallelism;
• Fast expert loading and unloading;
• Custom quantization kernels.

Deployers must consider both compute parallelism and VRAM allocation simultaneously. Data parallelism alone is far from sufficient, as a single weight shard can exceed the VRAM of a single GPU.

5. Local Deployment Hardware Requirements Estimation

5.1 VRAM is the Primary Constraint

Weights are stored in 4-bit format, occupying roughly 1.56TB. Although inference does not require loading all weights into GPU VRAM simultaneously, maximizing the resident proportion in VRAM remains critical for high-performance online serving.

A feasible estimation method is:

• Weight VRAM requirement: At least 1.5TB;
• Activation VRAM and KV cache: Depends on batch size and sequence length;
• System overhead and communication buffers: About 10% to 15% of total VRAM.

If using an 8-GPU server, the VRAM per GPU needs to be around the 300GB level. Currently, the options for high-end accelerator cards meeting this requirement are very limited and expensive.

5.2 Total Cost

Third-party tech communities estimate that an 8-GPU server capable of locally deploying this model might cost hundreds of thousands of dollars in hardware alone. This estimate only covers the main GPU server body and does not include:

• High-speed networking equipment;
• Storage arrays;
• Data center power and cooling;
• Operations and maintenance (O&M) and labor costs.

Therefore, for most teams, local deployment of this model is not the default choice. A more realistic path is:

• Short-term validation: Use on-demand rented GPU instances;
• Medium-term production: Evaluate dedicated clusters;
• Long-term control: Procure and build proprietary resources.

6. API Usage and Multi-Turn Tool Calling

If teams do not plan to deploy the model themselves, they can use it via API. The interface design is compatible with common conversational protocols, and the model name uses versioned naming.

6.1 Inference Intensity Control

The model supports configuring inference intensity, typically offering low, high, and maximum tiers. The highest tier is used by default. Higher inference intensity means the model performs longer internal reasoning before answering, which also increases token count and time-to-first-token latency.

Recommendations for different tasks:

• Simple Q&A, information extraction: Use low tier;
• Multi-step logical deduction: Use high tier;
• Complex math or coding tasks: Use maximum tier.

6.2 Required Content for Multi-Turn Tool Calling

In multi-turn tool calling scenarios, developers cannot merely retain the model’s final answer. In each request, alongside user input and tool results, the previous round’s reasoning content and tool call records must be passed back.

The reason behind this requirement is clear: tool calls may span multiple interaction turns. Without the complete reasoning context, the model cannot trace back “why a certain tool was called at that time,” thereby affecting the coherence of subsequent decisions.

In practical development, it is recommended to maintain a structured conversation state object on the server side, containing at least:

• User messages;
• Model replies;
• Reasoning content;
• Tool call parameters;
• Tool return results.

When making the next request, assemble these fields according to the protocol requirements and send them.

7. Deployment Strategy and Common Issues

7.1 Basic Flow from Download to Usability

For teams that genuinely require local deployment, the following workflow is recommended:

1. Confirm whether the business actually needs offline deployment;
2. Evaluate data security, latency, throughput, and cost requirements;
3. Select appropriate hardware, prioritizing VRAM capacity;
4. Download weights and verify shard integrity;
5. Prepare the inference framework environment;
6. Configure parallelism strategies and quantization parameters;
7. Conduct small-scale correctness validation;
8. Gradually increase concurrency and sequence length stress.

7.2 Easily Overlooked Issues

First, KV cache bloat. The model supports a context length of a million tokens, but under long sequences, the KV cache becomes a secondary VRAM black hole. It is necessary to set a reasonable maximum sequence length based on actual business scenarios.

Second, expert load imbalance. Under real-world traffic, sparse models may frequently invoke certain popular experts, causing increased load on a single GPU. Deployment requires monitoring the distribution of expert routing.

Third, multimodal input adaptation. If the business involves images, confirm the inference framework’s level of support for the vision encoder and the concatenation method for vision and text tokens.

8. Key Takeaways for Developers

This weight release is not just about having another downloadable model; it provides technical teams with an opportunity to observe the engineering implementation of ultra-large-scale sparse models.

For application developers, understanding the following judgments is more important:

• Model capability is not determined solely by total parameters; activated parameters, training data quality, and reinforcement learning strategies are equally critical.
• The deployment threshold for trillion-level models has shifted from “infeasible” to “high-cost feasible,” but the demand for business value is also higher.
• For the foreseeable future, sparse models will remain the primary route for balancing capability and cost.
• Deployment strategies must be comprehensively designed considering business latency, throughput, data compliance, and cost, rather than just looking at benchmarks or parameter scales.

If a team has the capability to deploy a model of this magnitude, it also possesses the ability to deeply customize and optimize smaller models. In other words, deployment capability itself is a scarce engineering skill.