Inference Adaptation and Optimization for Ultra-Large-Scale Mixture-of-Experts Models

Inference Adaptation and Optimization for Ultra-Large-Scale Mixture-of-Experts Models

As open-source large models move toward trillion-parameter scales and increasingly incorporate multimodal and long-context capabilities, the complexity of adapting underlying compute hardware and software stacks has risen exponentially. Recently, an open-weight model has performed strongly across multiple benchmarks, supporting million-token context while achieving top-tier results in code generation, 3D creation, and long-horizon agent tasks. For such ultra-large-scale MoE models, achieving Day-0 adaptation and establishing basic text and image inference pipelines has become a shared technical challenge for infrastructure providers and developers alike. This article sets aside specific product attributes and analyzes the inference adaptation path and optimization strategies for very large MoE models from a general technical perspective.

1. Core Challenges of Inference for Very Large MoE Models

Inference for trillion-parameter models is not simply a matter of stacking compute. The core difficulty lies in the dual constraints of the memory wall and the communication wall. On one hand, the enormous parameter count demands extremely high memory capacity and bandwidth. On the other hand, the expert routing mechanism in MoE architectures introduces frequent cross-GPU and even cross-node communication. In addition, the 1-million-token context length supported by the model causes the KV cache to grow explosively with sequence length, further intensifying memory pressure. At the same time, integrating multimodal visual computation requires the software stack to efficiently handle data inputs and attention computation logic across different dimensions. To address these challenges, relying solely on hardware-level compute scaling can no longer meet the demands of rapid iteration. Deep collaborative optimization across algorithms, operators, and compilation layers through a highly compatible software stack is essential.

2. A High-Compatibility Software Stack Enables Rapid Model Migration

The foundation for rapid model migration is a standardized software stack that covers programming models, runtime interfaces, and mainstream AI frameworks. In practice, teams typically build on widely used inference engines (such as the community edition of SGLang) to adopt upstream model implementations, then perform deep customization at the lower layers. This adaptation involves the coordination of several key components: operator libraries provide fundamental compute primitives, DSL compilers such as Triton and TileLang enable rapid implementation of custom operators, and device-side compilers translate high-level computation graphs into hardware-executable instruction sets.

Through this software stack, developers can efficiently convert community model code into inference services running on actual hardware. The complete stack covers four dimensions: framework execution scheduling, operator compilation and optimization, device-side runtime management, and multi-GPU cluster communication. This full-stack coverage allows new model adaptations to maximize reuse of mature low-level interfaces while focusing effort on optimizing newly added computation paths. For example, a unified runtime interface can abstract away underlying hardware differences, allowing upper-layer frameworks to directly call unified memory allocation and stream synchronization mechanisms.

3. Integrating Sparse Attention with the Visual Computation Pipeline

Long-context models typically adopt special attention mechanisms (such as the sparse attention DSA used by certain models) to reduce the computational complexity of long sequences. Adapting such mechanisms requires rebuilding the attention computation path from the ground up, primarily involving index selection, KV cache layout restructuring, and computation graph optimization for both the Prefill and Decode phases.

1. Index selection and KV cache layout optimization: In sparse attention, not all tokens need to participate in global attention computation. Teams need to integrate dedicated sparse attention operators into the operator library to dynamically generate and process sparse index matrices. Meanwhile, the KV cache can no longer use a traditional contiguous memory layout; a non-contiguous memory management strategy must be designed based on the sparsity pattern to ensure efficient reads of the required context information during the Decode phase.
2. Separate adaptation for Prefill and Decode: The Prefill phase is compute-intensive and suited to high-concurrency matrix operations, while the Decode phase is memory-access-intensive and requires memory access paths optimized for the sparsity pattern.
3. State management and visual attention fusion: For complex multi-layer network structures, a KDA (state manager) must be designed to coordinate the passing of cross-layer residuals. At the same time, the introduction of multimodal models means supporting both the full attention computation of the vision encoder and the sparse attention computation of the large language model. Both share a single underlying tensor computation and memory scheduling framework, ensuring a unified and efficient text and image inference pipeline.

4. Online Conversion and Reuse of Low-Precision Expert Weights

In MoE architectures, expert networks account for the vast majority of parameters. To deploy very large models within limited memory, the industry commonly uses lower-precision numerical formats (such as MXFP4) to store expert weights. However, directly executing extremely low-precision computation natively on hardware often risks incomplete operator support or precision overflow.

An efficient solution is “online conversion with cross-layer reuse of an FP8 workspace.” Specifically, the system does not need to pre-generate a complete and massive offline FP8 weight file. Instead, during the forward inference pass, it converts MXFP4-format expert weights on demand into the FP8 format, which has stronger hardware support.

This mechanism relies on fine-grained memory scheduling. Because expert computation in MoE models has sparse activation characteristics, only a subset of experts is active at any given time. The system reuses a fixed-size FP8 workspace across layers, taking turns converting the corresponding experts’ MXFP4 weights into that workspace for computation before each Transformer layer executes. This not only avoids the double storage overhead of generating offline FP8 weights but also greatly shortens model deployment preparation time, providing key technical support for agile deployment of ultra-large-scale parameter models.

5. Best Practices for Inference Service Deployment

When deploying trillion-parameter inference services in practice, beyond adapting the low-level software stack, the stability and resource scheduling of upper-layer services are equally critical. Enterprises typically rely on container platforms for elastic scaling of services, use object storage to host massive model weight files, and handle high-concurrency inference requests through message queues. On the monitoring side, log services and alerting systems must be combined to track GPU memory levels, cross-node communication latency, and throughput at each Prefill/Decode stage in real time. Only by deeply integrating the compute characteristics of underlying hardware, the compilation optimization capabilities of the software stack, and cloud-native infrastructure can ultra-large models achieve stable deployment in complex business scenarios.