Speculating Experts Accelerates Inference for Mixture-of-Experts
arXiv:2603.19289v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models have gained popularity as a means of scaling the capacity of large language models (LLMs) while maintaining sparse activations and reduced per-token compute. However, in memory-constrained inference settings, expert weights must be offloaded to CPU, creating a performance bottleneck from CPU-GPU transfers during decoding. We propose an expert prefetching scheme that leverages currently computed internal model representations to speculate future experts, enabling memory transfers to overlap with computation. Across multiple MoE architectures, we demonstrate that future experts can be reliably predicted by these internal representations. We also demonstrate that executing speculated experts generally maintains downstream task accuracy, thus preserving more effective compute-memory overlap by eliminating the need to re-fetch true router-selected experts. Integrated into an optimized inference engine, our ap
arXiv:2603.19289v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models have gained popularity as a means of scaling the capacity of large language models (LLMs) while maintaining sparse activations and reduced per-token compute. However, in memory-constrained inference settings, expert weights must be offloaded to CPU, creating a performance bottleneck from CPU-GPU transfers during decoding. We propose an expert prefetching scheme that leverages currently computed internal model representations to speculate future experts, enabling memory transfers to overlap with computation. Across multiple MoE architectures, we demonstrate that future experts can be reliably predicted by these internal representations. We also demonstrate that executing speculated experts generally maintains downstream task accuracy, thus preserving more effective compute-memory overlap by eliminating the need to re-fetch true router-selected experts. Integrated into an optimized inference engine, our approach achieves up to 14\% reduction in time per output token (TPOT) over on-demand loading of experts from CPU memory. For MoEs where speculative execution alone yields suboptimal accuracy, we further examine lightweight estimators that improve expert prediction hit rates, thereby reducing performance degradation. Our code is released in open-source at https://github.com/axonn-ai/yalis/tree/offload_prefetch.
Executive Summary
The article proposes an expert prefetching scheme for Mixture-of-Experts (MoE) models that leverages internal model representations to speculate future experts. This approach enables memory transfers to overlap with computation, reducing CPU-GPU transfer times and improving inference performance. By demonstrating reliable expert prediction and minimal accuracy degradation, the authors achieve up to 14% reduction in time per output token (TPOT) over on-demand loading. The code is released open-source for further development. This contribution has significant implications for the scaling of large language models (LLMs) and their deployment in memory-constrained environments. The proposed scheme has the potential to improve the efficiency and effectiveness of MoE models, enabling their wider adoption in various applications.
Key Points
- ▸ Proposes an expert prefetching scheme for MoE models to speculate future experts
- ▸ Demonstrates reliable expert prediction and minimal accuracy degradation
- ▸ Achieves up to 14% reduction in TPOT over on-demand loading
- ▸ Released open-source code for further development
Merits
Strength in Scalability
The proposed scheme enables the efficient scaling of MoE models in memory-constrained environments, making them more suitable for large-scale applications.
Improved Inference Performance
By reducing CPU-GPU transfer times, the scheme improves inference performance and enables faster processing of MoE models.
Demerits
Limitation in Accuracy
While the scheme demonstrates minimal accuracy degradation, it may not be suitable for applications requiring high accuracy, and further research is needed to improve expert prediction hit rates.
Potential Overhead
The scheme may introduce additional computational overhead due to the prediction and selection of future experts, which needs to be carefully optimized for real-world applications.
Expert Commentary
The article presents a novel and innovative approach to improving the inference performance of MoE models in memory-constrained environments. By leveraging internal model representations to speculate future experts, the authors demonstrate a significant reduction in CPU-GPU transfer times and improve inference efficiency. While the scheme shows minimal accuracy degradation, further research is needed to improve expert prediction hit rates and address potential overhead. The proposed scheme has significant implications for the deployment of MoE models in various applications and industries, making it a valuable contribution to the field of natural language processing.
Recommendations
- ✓ Further research is needed to improve expert prediction hit rates and address potential overhead.
- ✓ The proposed scheme should be integrated with other optimization techniques to further improve inference performance and efficiency.
Sources
Original: arXiv - cs.AI
