Open-Source Trillion-Parameter MoE Models: Architecture, Quantization, and Deployment

A leading research team recently released the full weights of its latest-generation large language model, along with detailed architecture parameters, multi-dimensional benchmark results, and the full text of its custom license. The model uses a Mixture of Experts (MoE) architecture with 2.8 trillion total parameters, the largest among currently open-weight models. This release marks another step toward opening ultra-large-scale models to the open-source community and provides valuable foundational infrastructure for deep customization and private deployment of enterprise AI applications. This article sets aside brand-specific details and analyzes the model’s architecture, quantization strategy, capability improvement path, and deployment approach from a purely technical perspective.

1. Ultra-Large-Scale MoE Architecture and Sparse Activation

The model inherits the mature K3-series architecture, with a total parameter count of 2.8 trillion. To address the compute challenges of such scale, it adopts the sparse activation mechanism of a Mixture of Experts (MoE) architecture. Specifically, the model does not activate all parameters during inference. Instead, a routing network dynamically selects the expert sub-networks best suited to the current token, activating only about 104 billion parameters per pass.

This design delivers significant engineering advantages:

1. Compute decoupling: The model’s total knowledge capacity correlates with total parameter count (2.8T), while the computational complexity of a single inference pass correlates only with the activated parameter count (104B). This allows the model to retain strong generalization while keeping inference compute requirements close to those of traditional dense models.
2. Long-context support: The model natively supports a 1 million token context window. Under the MoE architecture, the attention computation overhead for processing extremely long texts is distributed, and combined with sparse activation, this makes high-density information processing scenarios such as long document parsing and codebase-level analysis feasible.

2. MXFP4 Low-Precision Quantization and VRAM Optimization

For a model with 2.8 trillion total parameters, storing weights at the industry-standard 16-bit precision (BF16/FP16) would require approximately 5.6 TB. This is untenable for the VRAM of any single node. To address this, the model weights use MXFP4 low-precision quantization, reducing storage volume to 1.56 TB.

MXFP4 is a low-precision floating-point format based on block-level quantization. Its core logic is as follows:

• Block-level scaling: The weight matrix is divided into multiple fixed-size blocks (e.g., 32x32), with each block sharing a single scaling factor. This preserves numerical dynamic range while reducing the bit width required per weight.
• Precision retention: Compared with traditional INT4 integer quantization, MXFP4 retains the exponent bits of floating-point numbers, better fitting the long-tail characteristics of large model weight distributions and reducing performance loss from quantization.

In actual deployment, the 1.56 TB quantized weights must be loaded through a distributed inference framework. Enterprises typically use cloud container platforms and object storage services to cache weight shards on high-speed local disks and use RDMA networks for efficient parameter synchronization across multi-node GPUs.

The following is a generic pseudocode example for deploying a quantized MoE model based on a mainstream distributed inference framework:

import torch
from distributed_inference import MoELoader, EngineConfig
# 1. Configure distributed inference parameters
config = EngineConfig(
    tensor_parallel_size=8,   # Tensor parallelism, splits activated parameters across GPUs
    expert_parallel_size=16,  # Expert parallelism, distributes 2.8T total parameters across nodes
    quantization_config={
        "type": "mxfp4",
        "block_size": 32,
        "storage_dtype": "float8_e4m3fn" # Underlying storage type
    },
    max_seq_len=1000000       # Enable 1M token context window
)
# 2. Load quantized distributed weights
model_path = "oss://cloud-bucket/models/moe-2.8t-mxfp4/"
loader = MoELoader(config)
model = loader.load_weights(model_path)
# 3. Run long-text inference test
with torch.no_grad():
    prompt = "Analyze dependency relationships in a 1M-token codebase..."
    output = model.generate(prompt, max_new_tokens=2048)
    print(output)

3. Post-Training Reinforcement Learning and Capability Gains

According to the released model card, the model’s core capability improvements over the previous generation do not mainly come from expanded pretraining corpora, but from significantly increased reinforcement learning (RL) during post-training.

In the post-training stage, the model achieves capability gains through the following mechanisms:

1. Reward model alignment: Multi-dimensional reward models are built to score output quality in different scenarios such as work, coding, and chat, guiding the model to generate high-quality responses aligned with human preferences.
2. Long-horizon agent session optimization: Traditional RLHF is often limited to single-turn interaction, whereas this model introduces long-horizon agent session environments into reinforcement learning. The model must continuously revise its policy across multi-turn tool calls, code execution, and environmental feedback, gaining stronger robustness in complex task decomposition and execution.

On the third-party authoritative benchmark AgentArena, based on data from over 17,500 real-user long-horizon agent sessions, the model (Max version) ranks 13th among all evaluated models and ranks first among open-weight models across all three core dimensions: work, coding, and chat. Its net improvement rate reached +5.1%, compared with only +0.9% for the previous generation. This data directly reflects the substantial boost that post-training RL strategies provide to the model’s agent capabilities.

4. Enterprise Deployment Architecture and Cloud Integration

When enterprises decide to adopt such an ultra-large-scale open-weight model, they must focus not only on the model’s parameter metrics but also on building a complete engineering deployment architecture. Leveraging general infrastructure capabilities from cloud providers such as Baidu AI Cloud, enterprises can build the following highly available inference architecture:

1. Compute and network layer: Use cloud bare-metal servers equipped with high-performance GPU clusters, and RoCE v2 lossless networking to achieve microsecond-level inter-node communication, meeting the frequent communication demands of expert routing in MoE architectures.
2. Storage and distribution layer: The loading time of a 1.56 TB quantized model file directly affects the speed of service elastic scaling. By storing model weights in object storage and combining it with a distributed file system for near-end caching, multi-node parallel loading time can be reduced to minutes.
3. Monitoring and logging services: When handling 1M-token long-context requests, VRAM fragmentation or KV Cache overflow can easily occur. Professional monitoring and alerting systems must be deployed to track GPU VRAM utilization, active expert distribution hit rates, and request queue latency in real time, ensuring system stability under high concurrency.

5. Summary and Outlook

The release of full weights for a 2.8T-parameter MoE model with 104B activated parameters represents the current frontier of open-weight large models in terms of scale and engineering optimization. By compressing storage to 1.56 TB through MXFP4 quantization and combining it with deep optimization via post-training reinforcement learning, the model demonstrates outstanding performance in complex agent scenarios such as coding and work.

For developers and enterprise architects, understanding the underlying logic of sparse activation and low-precision quantization is a prerequisite for mastering such ultra-large-scale models. In the future, as distributed inference frameworks continue to evolve and cloud infrastructure continues to iterate, trillion-parameter models will likely play a core driving role in private deployments across more vertical industries.