Open-Source Trillion-Parameter LLM: Architecture, Quantization, and Deployment Guide

The Weight Release: A Milestone for Open-Source Large Models

In early November 2025, a leading AI company fulfilled its commitment by uploading the complete weights of its flagship large language model to major global model hosting platforms. This move is not merely a headline event for the technical community—it marks the official arrival of open-source large models in the “trillion-parameter era.” The model features approximately 2.8 trillion total parameters, roughly 104 billion activated parameters per token, a mixture-of-experts architecture, support for 1-million-token long context, and multimodal text-and-image input. Open weights mean that any team with sufficient compute resources can independently download, deploy, fine-tune, and even conduct security audits—a development with profound implications for democratizing AI technology.

Deep Dive into Model Architecture: The Shift from Dense to Sparse

Parameter Efficiency Revolution Under Mixture-of-Experts

The model adopts a classic mixture-of-experts (MoE) architecture, designed around the principle that “total parameters are large, but only a subset is activated per inference.” Specifically, the model contains 93 network layers in total, of which 69 use KDA attention layers and 24 use Gated DSA layers. This layered design is not arbitrary—it reflects systematic consideration of how different attention mechanisms perform across long-context tasks.

For the expert layers, the model configures 896 experts, dynamically selecting 16 experts per token during computation, plus 2 additional shared experts to capture globally common features. This sparse activation strategy means that although total parameters reach 2.8 trillion, the compute cost of a single forward pass is equivalent to a dense model of roughly 104 billion parameters—striking a refined balance between inference cost and model capability.

Attention Mechanism Evolution: From Full Attention to Sparse Attention

Compared to the previous generation’s full-attention Gated MLA layer design, the new version swaps in Gated DeepSeek Sparse Attention across most layers. The rationale is clear: when the context window expands to the million-token level, full attention’s computational complexity grows quadratically, whereas sparse attention structurally selects a subset of positions to attend to, dramatically reducing complexity while using gating mechanisms to ensure key information is still captured. This improvement makes scenarios such as long-document understanding, large-scale code repository analysis, and complex multi-turn conversations practically viable.

Multimodal Vision Encoding: Lightweight Without Compromise

The vision component integrates a MoonViT-V2 vision encoder with approximately 400 million parameters, responsible for converting image inputs into vector representations in a unified semantic space with text. Although the vision module accounts for a small share of total parameters, its coordination efficiency with the language backbone directly determines the model’s overall multimodal expressiveness in mixed image-text understanding tasks. This “lightweight vision tower plus heavy language backbone” design is one of the mainstream technical approaches for current multimodal large models.

Quantization-Aware Training: The Key to Deploying Ultra-Large Models

Mixed-Precision Scheme with MXFP4 Storage and MXFP8 Activation

The model weights are stored in MXFP4 format, while activations are computed at MXFP8 precision. MXFP4 refers to a 4-bit floating-point format whose numerical representation range is more flexible than traditional integer formats, maintaining good dynamic range at extremely low bit widths. Keeping activations at 8-bit precision effectively mitigates precision loss in gradient propagation and intermediate result representation under low precision.

More notably, the official announcement explicitly states that a quantization-aware training strategy was adopted. This means quantization error is explicitly modeled during the training phase, and model parameters continuously adapt to low-bit representation constraints during updates—rather than applying “post-hoc quantization” after training is complete. This approach of addressing quantization from the training source significantly reduces the performance degradation caused by low-bit quantization, making practical use of 4-bit stored weights feasible.

Storage Efficiency and Deployment Cost: A Balanced Analysis

Thanks to the extreme quantization strategy, although the repository occupies approximately 1.56 TB of storage on model hosting platforms and the parameter count in safetensors format is approximately 2.78 trillion, this represents over 70% storage savings compared to the at least 5.6 TB required for FP16 format storage. This advantage has significant practical value in scenarios such as post-distributed-training processing, multi-replica deployment, and model version iteration.

Inference Ecosystem Adaptation: Synchronized Multi-Framework Support

Alongside the weight release, multiple mainstream inference frameworks provided deployment solutions within a short timeframe. vLLM, SGLang, and the emerging TokenSpeed project have all completed adaptation to this architecture, meaning developers do not need to build an inference stack from scratch and can directly build on mature frameworks for secondary development or performance tuning.

Different frameworks have different adaptation priorities: vLLM excels in throughput and batch processing efficiency, suitable for high-concurrency serving scenarios; SGLang features strengths in frontend language and structured generation, suitable for complex Chain-of-Thought tasks; TokenSpeed focuses on extreme low-latency optimization, suitable for real-time interactive applications. The model provider simultaneously promoting multi-framework adaptation greatly reduces developers’ selection risk and technology lock-in concerns.

Hardware Requirements in Practice: Who Can Actually Deploy This?

Minimum VRAM Configuration

Although quantization has compressed storage to the extreme, running a 2.8-trillion-parameter MoE model still requires extremely high hardware investment. According to estimates from third-party community enthusiasts, one feasible server configuration requires 8 high-end accelerator cards of a certain brand, with total VRAM reaching approximately 2.3 TB. Based on current market prices, a single server’s hardware cost is estimated at approximately $550,000 to $650,000. The model provider has not given official hardware recommendations, which may suggest that current deployment costs remain a “privilege” of a minority of institutions and enterprises.

Cloud-Native Deployment and the Serverless Trend

Facing high hardware barriers, the more pragmatic path for individual developers and small-to-medium teams is to use cloud API services. The model service interface is compatible with two major industry API specifications, allowing developers to switch seamlessly. The API also offers Standard and High-Speed service tiers, with the high-speed version reportedly delivering 2 to 3 times the throughput of the standard version, suitable for Agent-type applications sensitive to response time. Additionally, calls support setting different inference effort levels—low, high, and maximum—enabling developers to make flexible trade-offs between quality and cost. For multi-turn tool-calling scenarios, the API requires returning the previous turn’s reasoning content along with tool call results to ensure complete state tracking.

Evaluation System Reconstruction: Measuring Agent Capabilities with Real Conversation Data

Evolution from Static Benchmarks to Dynamic Interaction Evaluation

Traditional offline evaluation sets (such as MMLU, HumanEval, etc.) have gradually lost discriminative power amid severe model homogenization. This model achieved impressive results in Agent Arena, an evaluation system based on real user conversation data. Agent Arena extracts multi-dimensional signals from real user Agent interaction sessions: whether users explicitly confirm task success, user praise and complaint text, the model’s ability to correct errors after user corrections, and whether shell commands execute successfully. These signals are combined to compute a “net improvement” score, which more faithfully reflects the model’s practical value in complex Agent workflows.

Score Dimension Analysis: Balancing Reliability and Task Generalization

In evaluations based on over 17,500 real long-horizon sessions, the model achieved a net improvement score of +5.1% at maximum inference effort setting, ranking first among open-weight models and thirteenth among all models. It holds the top spot among open models across all three task categories: Work, Code, and Chat. Particularly noteworthy is its performance in the Bash operation reliability dimension: a score of +7.9% ranks first among open models, whereas the previous generation scored −4.5% in this category, ranking thirteenth. This leap demonstrates that the new generation has achieved qualitative progress in Agent core capabilities including command generation, execution result understanding, and error repair.

Technical Decision Insights: Deployment Path Considerations for Open-Source Ultra-Large Models

Open Source or Closed Source? Weighing Capability Against Resources

This weight release event raises a thought-provoking question for the industry: institutions truly capable of using trillion-parameter open-source models remain a minority globally. The deeper significance of open weights lies in providing irreplaceable possibilities for academic research, security auditing, domain fine-tuning, and private deployment—when an organization holds the model’s key weights in its own hands, it gains greater autonomy in data security and compliance.

From Model Selection to Systems Engineering: Comprehensive Considerations

For teams considering deployment, we recommend technical evaluation across the following dimensions: First, confirm whether the infrastructure budget covers single-machine multi-GPU configurations, or whether there is capacity to rent cloud instances with equivalent compute specifications. Second, verify whether the application scenario genuinely requires million-token-level context and Agent-level capabilities, avoiding resource waste from “using a cannon to kill a mosquito.” Third, assess whether the precision impact of quantization-aware training falls within acceptable business ranges, conducting A/B comparison tests on business datasets when necessary. Fourth, monitor ongoing inference framework optimization developments, as performance differences across frameworks on different hardware and in different scenarios may significantly affect final service quality.

Conclusion: A “Cambrian Explosion” Moment for Open-Source Large Models

Each large-scale weight release pushes the boundary of AI capabilities further toward a broader developer community. From a technical evolution perspective, the combination of 2.8-trillion-parameter MoE architecture, quantization-aware training, and sparse attention has already charted a viable direction for efficient training and inference of next-generation large models. Although current hardware costs remain high, with continuous底层 optimization of inference frameworks, ongoing iteration of specialized accelerator chips, and model architectures evolving toward more efficient sparsity, the path for trillion-parameter models to reach mainstream application scenarios is already clear. For technical decision-makers, now is the optimal time to develop a deep understanding of these frontier architectures and evaluate their medium-to-long-term application potential.