Deploying and Evaluating Ultra-Large MoE Models: A Guide to Open-Weight Practices

1. Background: Why Open Weights Are a Watershed Moment

The competitive landscape of large language models is shifting from “parameter scale” to “accessibility.” Even if a model performs exceptionally well on benchmarks, if it is only accessible via a closed API, enterprises cannot fine-tune, audit, or privately deploy it. This makes data sovereignty and cost control impossible. Consequently, whether a model offers “open weights” has become a key differentiator—it determines whether a model can only be “called” or genuinely “owned.”

Recently, a sparse Mixture-of-Experts (MoE) model with a total parameter count of 2.8 trillion officially released its complete weights to the community, along with deployment recipes and third-party evaluation results. This article breaks down the implementation path for such ultra-large models across five dimensions: architectural specifications, quantization and storage, deployment thresholds, API integration, and real-world scenario evaluations.

2. Model Architecture: Key Designs for Sparse Activation and Long Context

2.1 Total Parameters and Activation Scale

The model has a total parameter count of approximately 2.78 trillion, but the actual activated parameters per token are only around 104B. This design—featuring a massive total parameter count with a controllable per-token activation size—is the core value of the MoE architecture: trading immense knowledge capacity for computational economy during inference.

From an engineering perspective, this implies two things:

• VRAM usage is dictated by the total parameter count, meaning weights must either fully reside in memory or be sharded.
• Per-token computation is dictated by the activated parameters, resulting in inference throughput significantly higher than that of a dense model with the same total parameter count.

2.2 Layer Structure and Attention Mechanism

The model card specifies a structure of 93 layers, of which 69 adopt the KDA structure and 24 use Gated Sparse Attention. Compared to the previous generation, which uniformly applied Gated Multi-Head Latent Attention (Gated MLA) across all attention layers, this generation replaces some layers with Gated Sparse Attention. The objective is clear: reduce attention computation overhead in ultra-long context scenarios.

The cost of long-context modeling primarily stems from the quadratic growth of the attention matrix. Sparse attention restricts the key-value range each token attends to, reducing complexity from quadratic to nearly linear. The trade-off is the need for more refined gating designs to prevent information loss.

2.3 Expert Layer Configuration

The expert layer consists of 896 experts. For each token, 16 are selected via routing, and 2 are shared experts. Shared experts carry general knowledge, preventing all tokens from relying solely on routing results, thereby improving training stability and inference consistency.

2.4 Vision Encoder Module

The vision component is a Vision Transformer variant with approximately 401M parameters, supporting image-text mixed inputs. This enables the model to process rich media content such as screenshots, document images, and charts, which is particularly crucial for AI Agent applications.

3. Quantization Strategy: MXFP4 Storage and MXFP8 Activation

3.1 Format Selection

Weights are stored in the MXFP4 format, and activations use MXFP8. The core philosophy of the MX (Microscaling) format family is sharing a single scaling factor within extremely small blocks, thereby maintaining the dynamic range of numerical distributions even at narrow bit-widths like 4-bit or 8-bit.

Official documentation notes that the model employs Quantization-Aware Training (QAT), simulating quantization errors during the training phase rather than applying Post-Training Quantization (PTQ). The practical significance of this is that the precision loss from low-bit storage is compensated in advance, ensuring that inference output quality closely resembles the full-precision version.

3.2 Storage Overhead

According to the public repository interface, the weight repository occupies approximately 1.56TB, corresponding to the roughly 2.78 trillion parameters. The 1.56TB figure is notable: storing 2.78 trillion parameters in FP16 would theoretically exceed 5TB. MXFP4 compresses this to roughly 1.56TB, achieving a compression ratio of nearly 4x.

For deployment teams, this means:

• A single machine’s disk or high-speed storage must reserve at least 2TB of space;
• Weight loading time is directly tied to storage bandwidth, making high-throughput local NVMe or distributed caches highly recommended;
• In multi-node deployments, weight distribution itself becomes a network engineering challenge that requires careful planning.

4. Deployment Path: Inference Frameworks and Hardware Thresholds

4.1 Adaptation of Mainstream Inference Frameworks

Several mainstream high-performance inference frameworks now provide deployment recipes covering common combinations of Tensor Parallelism (TP), Pipeline Parallelism (PP), and Expert Parallelism (EP). A typical deployment workflow is as follows:

# Pseudocode example: Loading with Tensor Parallelism + Expert Parallelism
# Step 1: Verify the integrity of weight shards
# Step 2: Configure parallelism degrees (TP/EP/PP)
# Step 3: Specify quantization format and KV Cache strategy
# Step 4: Start the service and verify long-context capabilities

When selecting a framework, focus on three key aspects: native support for MXFP4 weight loading, support for Expert Parallelism to distribute the VRAM pressure of the 896 experts, and whether paged management is implemented for long-context KV Cache.

4.2 Hardware Threshold Estimation

The hardware threshold for local deployment is exceptionally high. According to third-party estimates, a feasible configuration involves a server with 8 high-end data center GPUs totaling approximately 2.3TB of VRAM, with hardware costs ranging from $550,000 to $650,000. It must be emphasized that these are third-party estimates; the official provider has not released formal hardware recommendations.

The logic behind these numbers:

• Even with 4-bit storage, 2.78 trillion parameters require over 1.4TB of VRAM to fully reside in memory;
• Adding KV Cache, activation buffers, and framework overhead, a 2TB-class VRAM capacity is the realistic starting point;
• Utilizing CPU offloading or disk swapping will significantly degrade throughput, making it unsuitable for production-grade Agent workloads.

For most teams, directly calling the API is a more pragmatic choice.

5. API Integration: Compatibility and Inference Parameters

5.1 API Compatibility

The model offers two API model identifiers: a standard version and a high-speed version. The high-speed version is officially stated to be 2 to 3 times faster than the standard version. At the API level, it is compatible with both OpenAI and Anthropic specifications, ensuring low migration costs.

5.2 Reasoning Effort Control

The reasoning_effort parameter offers three tiers: low, high, and max (default is max). This parameter directly dictates the “thinking” budget the model invests before providing a final answer. For simple tasks, lowering the tier can significantly reduce latency and token consumption; for complex, long-horizon tasks, the max tier provides greater stability.

5.3 Key Details for Multi-Turn Tool Calling

In multi-turn tool calling scenarios, there is a crucial but easily overlooked convention: you must pass back the reasoning content and tool call records from the previous turn. Otherwise, the model will lose the intermediate reasoning chain, leading to repeated calls or logical breaks.

// Example: Retaining reasoning and tool call fields in multi-turn requests
{
  "messages": [
    {"role": "user", "content": "..."},
    {"role": "assistant", "reasoning_content": "...", "tool_calls": [...]},
    {"role": "tool", "content": "..."}
  ]
}

This design indicates that the model operates with stateful progression in Agent scenarios. Developers must maintain the complete session context on the client side.

6. Real-World Scenario Evaluation: From Session Signals to Net Improvement

6.1 Evaluation Data Sources

Unlike traditional static benchmarks, data from a certain evaluation platform is derived from real user Agent sessions. It extracts several types of signals from these conversations:

• Whether the user explicitly confirms task success;
• User compliments and complaints regarding the model;
• The response quality of the model after user corrections;
• Whether shell commands execute successfully.

Based on these, a “net improvement” metric is calculated, representing the positive benefits brought by the model relative to the baseline, minus the negative losses.

6.2 Evaluation Results

Based on over 17,500 real long-horizon sessions, the model (at the max tier) achieved a net improvement of +5.1%, ranking 1st among open-weight models and 13th overall. It secured the 1st position among open-weight models across the Work, Code, and Chat categories. The previous generation model at the same tier recorded +0.91%, ranking 6th among open-weight models.

In the sub-item data, Bash reliability reached +7.9%, ranking 1st among open-weight models, whereas the previous generation recorded -4.5%, ranking 13th. This sub-item showed the most significant improvement, indicating that this generation has achieved substantial stability enhancements in command-line environments. For Agents, a failed shell command often means the entire task chain is broken; thus, the value of improved reliability far exceeds simple conversational quality improvements.

7. Practical Recommendations

Determine if Local Deployment is Necessary: If tasks involve sensitive data, require fine-tuning, or have extremely high call volumes, local deployment makes economic sense; otherwise, the API is the superior solution.

Pre-deployment Checklist:

1. Storage: Reserve over 2TB of high-speed storage;
2. VRAM: Plan for a 2TB-class capacity, prioritizing Expert Parallelism;
3. Framework: Ensure support for MXFP4 weights and long-context KV Cache paging;
4. Network: Plan weight distribution bandwidth for multi-node deployments.

API Usage Considerations:

1. Use the max tier for long-horizon Agent tasks;
2. Always pass back reasoning content and tool call records during multi-turn tool calling;
3. Switch to the high-speed version for latency-sensitive scenarios.

The significance of releasing open weights for ultra-large models goes beyond mere “downloadability.” It allows enterprises, for the first time, to fully control, audit, and optimize a 2.8T-class sparse model on their own infrastructure. The barrier to entry for this path remains high, but it has transitioned from “impossible” to “plannable.”