Deploying and Evaluating a Trillion-Parameter Open-Weight MoE Model: Architecture, Infrastructure, and Benchmarks

Open-weight models are increasingly becoming a practical option for teams that want to build their own inference capabilities. When a Mixture-of-Experts model with roughly a trillion total parameters and around 100 billion activated parameters per token becomes fully downloadable, the technical significance goes well beyond “one more model to try.” It means mid-sized teams can, for the first time, deploy, fine-tune, and audit a near-frontier sparse model in their own environment. This article provides a systematic technical analysis across six dimensions: model architecture, quantization, deployment paths, hardware requirements, API usage, and third-party evaluation.

1. Model Architecture: Sparse Experts Combined with Sparse Attention

According to the model card, this generation contains 93 layers in total. Of these, 69 layers use a linear-attention-style structure referred to as KDA, while 24 layers use gated sparse attention, or Gated DSA. Compared with the previous generation, the most notable change is that the original full-attention gated MLA layers have been replaced with gated sparse attention layers. The motivation is clear: in long-context modeling, the dominant memory and compute costs come from the attention matrix. By restricting the key-value range that each token attends to, sparse attention brings the cost of million-token contexts down to a manageable level.

On the expert side, the model has 896 experts in total. Each token is routed to 16 experts, and 2 shared experts are always active. This “many experts, few activations” design has become the mainstream approach for trillion-scale MoE models: total parameters determine model capacity, while activated parameters determine the per-token compute cost. With 104B activated parameters, the actual computation per token is roughly comparable to a dense model in the tens-of-billions range, which leaves room for reasonable inference throughput despite the enormous total size.

The vision component uses a ViT variant with approximately 401M parameters and supports mixed image-text inputs. For agent scenarios that involve screenshots, document images, or chart understanding, this capability is a requirement rather than a nice-to-have.

2. Quantization: MXFP4 Storage with MXFP8 Activation

The weights are stored in MXFP4 format and activations are computed in MXFP8. The official documentation states that quantization-aware training (QAT) was used during training. These choices deserve closer examination:

• 4-bit floating-point storage: If a trillion-scale model were stored in BF16, the weights alone would require multiple terabytes of disk space. MXFP4 reduces the storage footprint to roughly one quarter, bringing the full weight repository to around 1.56TB. The safetensors metadata reports approximately 2.78 trillion parameters, consistent with the nominal 2.8T figure. For download and on-disk storage, this is the difference between infeasible and feasible.
• 8-bit floating-point activation: Activation values have a wide dynamic range. Applying 4-bit precision directly to activations would cause significant accuracy loss, so the compute path keeps 8-bit precision. This is a typical engineering trade-off that separates storage precision from compute precision.
• Quantization-aware training: This is a key point. Simulating quantization error during training, rather than applying post-training quantization (PTQ), is better at preserving accuracy for long-context modeling and sparse routing. Expert routing in particular is sensitive to numerical perturbation.

One caveat: MXFP4 and MXFP8 are block floating-point formats that require kernel support in the inference framework. Before deployment, confirm that the target framework version covers the relevant quantization paths. Otherwise, the model may fall back to dequantization before computation, significantly reducing the performance benefit.

3. Deployment Paths: Framework Support Status

Major inference frameworks have published deployment recipes covering high-throughput serving, research-oriented deployment, and low-latency scenarios. A typical workflow can be summarized as:

# 1. Download the weights (the repository is about 1.56TB; plan disk space and bandwidth accordingly)
# 2. Confirm that the framework version supports the relevant quantized formats
# 3. Configure tensor parallelism and expert parallelism according to the recipe
# 4. Start the service and run long-context stress tests

Several practical pitfalls are worth noting:

1. Parallelism strategy: With 896 experts, expert parallelism (EP) must be combined with tensor parallelism (TP). If EP is too low, expert weights will overflow a single device. If EP is too high, communication overhead increases. Start with the values recommended in the framework recipe, then adjust based on measured throughput.
2. Long-context memory usage: KV cache is the dominant memory consumer for million-token contexts. Sparse attention reduces computation, but the KV cache does not disappear. Estimate memory based on actual concurrency and sequence length.
3. Quantization kernel alignment: Confirm that both the MXFP4 weight-loading path and the MXFP8 activation compute path are enabled in the framework. Avoid implicit dequantization.
4. Time to first token: MoE routing and expert scheduling introduce additional overhead. Interactive scenarios should monitor TTFT closely.

4. Hardware Requirements: Third-Party Estimates as a Practical Reference

The hardware barrier for local deployment is significant. Community estimates suggest one viable configuration is a server with 8 high-end accelerators and approximately 2.3TB of total memory, at a cost of roughly $550,000 to $650,000. It should be emphasized that this is a third-party estimate, not an official recommendation. For most teams, the more realistic options are:

• Self-hosted inference: Suitable for teams with stable high-concurrency demand and the necessary data center and operations capabilities.
• API access: Suitable for validation and small-to-medium workloads. Pay-as-you-go pricing avoids hardware and operational investment.
• Hybrid approach: Route sensitive core workloads through self-hosted infrastructure, and use API capacity for peak traffic.

5. API Usage Notes

The API model name is compatible with mainstream interface protocols. A high-speed variant is also available, which the provider states is 2 to 3 times faster than the standard version. The reasoning effort parameter accepts low, high, and max, with max as the default. For multi-turn tool calling, one detail is easy to miss: you need to pass back both the reasoning content and the tool call records from the previous assistant turn. Otherwise, the model loses the decision-making context and may repeat calls or break the logic flow. An example structure:

{
  "messages": [
    {"role": "assistant", "reasoning_content": "...", "tool_calls": [...]},
    {"role": "tool", "content": "..."}
  ],
  "reasoning_effort": "max"
}

6. Evaluation Results: Performance in Real Agent Sessions

A third-party evaluation platform calculated a “net lift” metric based on more than 17,500 real long-running sessions. The signals include explicit user confirmation of task success, praise and complaints, model responses after user corrections, and whether shell commands executed successfully. These signals are closer to production behavior than static benchmarks, though they are more susceptible to task distribution. Sample composition should be considered when interpreting the results.

This model (Max setting) achieved a net lift of +5.1%, ranking 1st among open-weight models and 13th overall. The previous generation scored +0.91% and ranked 6th among open models. By category, Bash reliability improved to +7.9%, 1st among open-weight models, compared with −4.5% and 13th place for the previous generation. The Work, Code, and Chat categories all ranked 1st among open-weight models. This data suggests that the replacement of full attention with sparse attention, combined with quantization-aware training, produced measurable improvements in long-horizon tool-calling scenarios that are prone to failure — particularly command execution reliability, which is one of the most underestimated aspects of agent deployment.

7. Implementation Recommendations

For teams preparing to adopt this model, the following sequence is recommended. First, run task-level evaluations through the API to confirm net lift on your specific workload distribution. Second, evaluate the throughput and cost model for self-hosted inference, with particular attention to memory usage under long-context concurrency. Finally, decide on a fine-tuning strategy. Full-parameter fine-tuning of a trillion-scale MoE model is extremely expensive, so adapter-based fine-tuning or freezing the routing layers are preferable starting points. At the same time, establish a regression test suite focused on tool-calling reliability, since this capability fluctuates the most between versions and has the most direct impact on user experience.