Trillion-Parameter Open-Weight MoE Models: A Complete Look at Structure, Deployment, and Evaluation

Full weight releases of large-scale Mixture-of-Experts (MoE) models are among the most closely watched events in the open-source community. When a model with trillion-level total parameters—yet activated parameters per token kept at the tens-of-billions level—uploads its complete weights to a public model repository, three paths previously blocked by closed APIs open at once: local deployment, domain fine-tuning, and model auditing. This article systematically examines the engineering of putting such ultra-large MoE models into production across six dimensions: model structure, quantized storage, deployment options, hardware requirements, API conventions, and evaluation methods based on real user sessions.

1. What Open Weights Actually Unlock

Previously, models of this class were available only as closed APIs—developers could call them but could not inspect them. With full weights released, teams gain three capabilities:

• Local deployment: run inference on your own compute with data never leaving your environment, well suited to compliance- and privacy-sensitive scenarios;
• Fine-tuning and distillation: adapt the full parameters to your domain, or distill smaller specialized models;
• Auditing and evaluation: inspect model behavior, reproduce benchmark results, and run safety analyses.

Note that open weights do not mean open training data or training code. The model card remains the primary reference for understanding the model’s capability boundaries.

2. Model Structure: Sparse Attention and Expert Routing

According to the model card, the model has 93 layers: 69 use a KDA architecture and 24 use Gated Sparse Attention. Compared with the previous generation, full-attention layers have been replaced with gated sparse attention to reduce attention compute and memory overhead in long-context modeling—at the million-token scale, full attention’s quadratic complexity quickly becomes the bottleneck, and sparsification is a common engineering trade-off.

On the expert side, there are 896 experts in total; each token routes to 16 of them, plus 2 shared experts. Shared experts carry general-purpose capability and reduce output instability caused by routing fluctuation—a common design choice in current MoE models. The vision component is a vision encoder of roughly 400 million parameters, supporting image-text input.

The core engineering implication of this structure: total parameter count determines the lower bound of memory usage, while activated parameters determine the compute cost of each inference pass. The combination of trillion-scale total parameters and tens-of-billions-scale activated parameters keeps inference compute relatively manageable but places extreme demands on memory capacity.

3. Quantized Storage: MXFP4 Weights + MXFP8 Activations

Weights are stored in MXFP4 format with MXFP8 activations, and the model provider states that quantization-aware training (QAT) was used. These choices deserve attention:

• 4-bit floating point for storage: compresses weight size to roughly one quarter of FP16, which is the precondition for distributing a trillion-parameter model at around 1.5TB;
• 8-bit floating point for activations: precision loss is more controllable than with 4-bit activations, which is friendlier to inference stability;
• Quantization-aware training: quantization error is simulated during training rather than applied as post-training quantization afterward, which typically reduces precision regression significantly.

Repository information shows the weight repository occupies about 1.56TB, with roughly 2.78 trillion parameters counted across the weight files—consistent with the nominal 2.8T total. One easily overlooked detail: download size and runtime VRAM footprint are not the same thing. After loading, weights are dequantized or rearranged into specific kernel layouts, so actual memory requirements often exceed the file size. You must leave headroom when planning hardware.

4. Deployment Paths: Choosing Among Three Inference Stacks

Mainstream open-source inference stacks have published deployment recipes, which fall roughly into three categories:

1. High-throughput serving stacks: strong at continuous batching and PagedAttention-style memory management, suited to API service scenarios;
2. Structured generation and low-latency stacks: deeper kernel optimization for sparse attention and MoE routing, suited to scenarios sensitive to first-token latency;
3. Hardware-specific acceleration stacks: operator fusion and custom quantization kernels targeting specific accelerator cards, maximizing throughput per unit of compute.

When selecting a stack, validate in this order:

1. Confirm the stack supports this model's sparse attention layers and MoE routing
2. Confirm it supports direct MXFP4 weight loading (avoiding a second conversion)
3. Confirm the tensor parallelism / expert parallelism sharding strategy against your memory budget
4. Run a small-scale load test: single-request latency, concurrent throughput,
   and the VRAM growth curve under long context

Because both the layer count and expert count are large, expert parallelism (EP) is usually mandatory, distributing different experts across different devices, combined with tensor parallelism to split intra-layer computation. This combination is highly demanding on interconnect bandwidth—both intra-node and inter-node interconnects become decisive factors for real-world throughput.

5. Hardware Requirements: Third-Party Estimates and Realistic Expectations

The hardware bar for local deployment is very high. Third-party developers have estimated that one viable configuration is a server with 8 high-end accelerator cards and about 2.3TB of VRAM, at a total cost of roughly $550,000 to $650,000. To be clear, this is a third-party estimate, not an official hardware recommendation—no official recommended configuration has been published.

From an engineering perspective, do your own budgeting with this logic:

• Memory floor: weights take about 1.56TB; adding KV cache, activation buffers, and communication buffers, more than 2TB of VRAM is a realistic starting point;
• Parallelism strategy: if you don’t have enough devices, you need to combine expert parallelism + tensor parallelism + pipeline parallelism—the finer the split, the higher the communication overhead;
• Long-context cost: KV cache usage for million-token contexts grows linearly with concurrency, and it is the most easily underestimated part of capacity planning;
• Total cost of ownership: beyond hardware purchase, factor in datacenter power, cooling, and operations costs.

For most teams, the “self-host vs. API” decision point is not model capability, but data compliance requirements and request volume. When request volume is too small to amortize hardware costs, the API is the more economical choice.

6. API Conventions: Details You Must Get Right

If you choose the hosted API, the model is offered under a standard name and a fast name, with the latter running roughly 2 to 3 times faster; the interface is compatible with mainstream OpenAI and Anthropic styles. Three common pitfalls in implementation:

First, the reasoning effort parameter. reasoning_effort accepts low, high, and max, defaulting to max. The default favors quality over latency—explicitly lower it for interactive scenarios.

Second, passing context back in multi-turn tool calls. In multi-turn tool calling, you must pass back the previous turn’s reasoning_content and tool_calls together; otherwise the model loses its reasoning chain, causing repeated tool calls or incorrect parameters. This is a high-frequency pitfall with models that expose a reasoning process.

Third, batching strategy for very long contexts. Million-token context is available, but cost and latency rise accordingly—in practice, split inputs by task rather than unconditionally filling the window.

7. Evaluation Logic: Why “Net Improvement on Real Sessions” Deserves More Attention

The model ranks near the top among open-weight models, and its evaluation data comes from real user Agent sessions rather than static question banks. Evaluators extract several types of signals from these sessions:

• whether the user explicitly confirmed the task succeeded;
• praise and complaints about the model;
• the model’s response quality after user corrections;
• whether shell commands executed successfully.

A “net improvement” metric is computed from these signals. Based on more than 17,500 real long-horizon sessions, the model (Max tier) achieved a net improvement of +5.1%, ranking first among open-weight models overall and first in the Work, Code, and Chat categories; the previous generation at the same tier was +0.91%. On the command-line reliability subscore it reached +7.9%, whereas the previous generation scored negative.

The value of this evaluation methodology is that it stays close to the real failure modes of long-horizon tasks: static benchmarks struggle to cover behaviors like “can the model self-correct after a tool call fails mid-task,” while real-session signals capture exactly that. For developers, it also points to the right way to evaluate for your own business—running small-sample regressions on long-horizon tasks from your own scenarios is more meaningful than looking at leaderboard rankings.

8. Adoption Recommendations

Taken together, putting this class of ultra-large MoE models into production can proceed in three steps:

1. API first, self-host later: validate business value through the hosted interface while accumulating prompt and tool-calling patterns;
2. Do the memory math: if self-hosting is truly necessary, first budget VRAM based on weight size + KV cache + buffers, then decide on the parallelism split;
3. Build your own evaluation set: sample tasks from real sessions and continuously track behavioral metrics such as net improvement and command-line reliability, rather than relying only on public leaderboards.

Releasing trillion-parameter weights effectively hands both “capability verification” and “cost bearing” to the user. The structure, quantization, and deployment options are relatively clear—the real barrier lies in memory budgeting and operational capability, which is also the core criterion for deciding whether self-hosting is worthwhile.