Adapting Trillion-Parameter Sparse MoE Models to AI Accelerators: From Architecture to Engineering

1. Background: A New Phase in Model Scale and Architectural Evolution

In recent years, the capability boundaries of large models have been pushed higher at a rapid pace. Parameter counts have moved from the tens of billions toward the trillion scale. In this process, purely dense architectures have struggled to balance performance with inference cost. Sparse Mixture of Experts (MoE) architectures have consequently become a mainstream choice for very large models. At the same time, multimodal capability is no longer treated as an add-on module. Instead, visual and language capabilities are jointly modeled from the pretraining stage, forming a “native multimodal” technical approach.

A recently released open-weight native multimodal model with a total size of 2.8 trillion parameters has been successfully adapted to a domestic AI accelerator platform. This effort reflects a complete technical chain for bringing ultra-large sparse models and specialized hardware together. This article starts from the architectural characteristics of the model, breaks down the key engineering problems in the adaptation process layer by layer, and presents reusable optimization approaches.

2. Model Architecture Analysis

2.1 Sparse Expert Architecture: Decoupling Parameter Count from Computation

The model adopts a Stable LatentMoE sparse expert architecture with a total of 2.8 trillion parameters and 896 routed experts. During inference, each token activates only 16 of these experts.

The core value of this design is that it decouples model capacity from per-inference computation:

• Capacity dimension: 2.8 trillion parameters give the model strong knowledge storage and representation capability.
• Computation dimension: Each token activates only 16 out of 896 experts, meaning that only a very small fraction of parameters actually participate in forward computation. This keeps per-inference compute overhead within an acceptable range.

From an engineering perspective, the system challenge shifts from “is there enough compute” to “how can experts be efficiently scheduled and how should communication be handled.” With 896 experts, the full model weights cannot reside in the memory of a single accelerator. Expert parallelism is required to distribute different experts across different devices, which introduces token dispatch and collection communication between devices.

2.2 Hybrid Attention: Combining Linear Attention and Sparse Attention

The model uses a hybrid attention design that combines KDA, a linear attention variant, with Gated DSA, a form of sparse attention. Approximately three quarters of the attention blocks use KDA linear attention, with an additional Top-2048 sparse attention mechanism. The model supports a context length of up to 1 million tokens and native image understanding.

The intent behind this combination can be understood at two levels:

First, linear attention controls computational complexity for long sequences.

Standard self-attention grows quadratically with sequence length, making it nearly infeasible at the 1-million-token scale. Linear attention reduces complexity to near-linear through kernel function approximation or state recurrence, making very long contexts computationally practical.

Second, sparse attention preserves precise modeling of key information.

Pure linear attention suffers from information loss in long-range precise retrieval. The Top-2048 sparse attention mechanism selects only the 2048 most relevant positions for exact attention computation at each token. This maintains long-context coverage while restoring precise modeling capability for critical segments.

Mixing the two at a ratio of approximately 3:1 is fundamentally an engineering balance between efficiency and precision.

2.3 Native Multimodality: From Add-On to Built-In

“Native multimodal” means that image understanding is not achieved by attaching a vision encoder after the fact. Instead, visual and text tokens are modeled jointly from pretraining. The engineering implications include:

• Image tokens are mixed into input sequences, further increasing sequence length and memory usage.
• Visual tokens and text tokens share the same attention and expert routing mechanisms, placing higher demands on operator generality.
• Preprocessing and batch scheduling for multimodal data become more complex.

3. Key Technical Challenges in Adapting to Domestic AI Accelerators

Deploying this architecture on domestic AI accelerators is not a simple model migration. It requires system-level re-engineering. The main challenges fall into four areas.

3.1 The Memory Wall and Expert Distribution

Even when stored at lower precision, the weights of a 2.8-trillion-parameter model far exceed the memory capacity of a single accelerator. The adaptation must therefore use a combination of expert parallelism, tensor parallelism, and pipeline parallelism:

• Expert parallelism splits the 896 experts across different device groups.
• Tensor parallelism further partitions the matrix operations within a single expert.
• Pipeline parallelism assigns different layers to different device stages.

When these three strategies are combined, communication patterns become highly complex. Bandwidth, latency, and memory usage must be repeatedly weighed against each other.

3.2 Communication Overhead in Expert Routing

A typical MoE inference flow works as follows: the gate network selects the top-K experts for each token, where K equals 16 in this case. Tokens are then sent to the devices hosting the selected experts through All-to-All communication, and results are collected after computation.

At the scale of 896 experts, the All-to-All communication volume is substantial. Optimization directions include:

• Token grouping and aggregation: Combine tokens destined for the same device into fewer transfers, reducing the number of communication operations.
• Communication-computation overlap: While waiting for communication results, execute operators that do not depend on those results.
• Routing load balancing: Prevent a small number of experts from becoming overloaded and turning certain devices into bottlenecks.

3.3 Operator Adaptation for Linear and Sparse Attention

KDA linear attention involves a state recurrence structure. Its computation pattern differs from standard matrix multiplication and requires dedicated operator implementations. Top-2048 sparse attention involves dynamic indexing and gather/scatter operations, which are unfriendly to standard memory access patterns.

On domestic AI accelerators, these operator types often lack mature official implementations. Custom development is therefore needed based on the specific hardware characteristics, including:

• Fused operator design for recurrence structures.
• Efficient sorting and deduplication of sparse indices.
• Blocked computation and memory reuse for long sequences.

3.4 Memory and Bandwidth Pressure at Million-Token Context Lengths

A 1-million-token context places extreme demands on storage for the KV cache, or for linear attention state. Even when linear attention compresses the state, the inclusion of image tokens further increases sequence length. Optimization measures include:

• Paged KV management with on-demand loading.
• State compression and quantization.
• Chunked context pre-filling to reduce peak memory usage.

4. Engineering Implementation Path and Best Practices

4.1 Example Parallel Strategy Configuration

The following is a typical parallel configuration approach, shown as illustrative pseudocode:

# Parallel dimension configuration example
parallel_config = {
    "expert_parallel_size": 64,    # 896 experts split across 64 ways
    "tensor_parallel_size": 8,     # tensor partitioning within each expert
    "pipeline_parallel_size": 4,   # pipeline partitioning across layers
    "sequence_parallel": True,     # long sequence partitioning
    "context_parallel_size": 2,    # context parallelism
}
# Total device count = 64 * 8 * 4 = 2048 (illustrative)

The actual configuration must be tuned repeatedly based on cluster topology, per-accelerator memory, and interconnect bandwidth.

4.2 Operator Optimization Recommendations

1. Prioritize fused operators: Combine the routing gate computation, Top-K selection, and token reordering into a single fused operator to reduce kernel launch overhead.
2. Optimize memory access: Perform sparse attention indexing operations in on-chip cache as much as possible to reduce global memory round trips.
3. Precision strategy: Store expert weights at lower precision while keeping critical accumulation paths at higher precision, balancing speed and stability.

4.3 Long-Context Tuning Points

• Use chunked parallelism during the pre-fill stage to avoid processing excessively long sequences in a single pass.
• Reuse linear attention state during the decoding stage to avoid redundant computation.
• Apply reasonable chunking policies separately to image tokens and text tokens.

5. Implications for Developers

Adapting ultra-large MoE models to specialized accelerators is fundamentally a process of matching architectural characteristics with hardware properties. For developers, several points are worth noting:

1. You need to understand the architecture before you can optimize the system. The expert distribution of MoE, the hybrid attention ratio, and the context length directly determine the direction of parallel strategy and operator design.
2. Communication is the core bottleneck. At the trillion-parameter scale, computation is often not the limiting factor. Inter-device communication is.
3. Custom operators are unavoidable. General-purpose operator libraries rarely cover newer structures such as linear attention and sparse attention. Deep customization is a necessary step.
4. Long-context support is a systems engineering problem. From memory management to operator blocking, every layer requires targeted design.

As the scale of open-weight models continues to grow and the software stacks of domestic AI accelerator platforms mature, this kind of joint adaptation between large models and specialized hardware will become a routine engineering practice. Understanding the underlying methodology is more valuable in the long run than memorizing any specific configuration.