Enhancing the Efficacy of MoE Offloading with Speculative Prefetching Strategies

[yihong1120]

Dear Mixtral Offloading Contributors,

I hope this message finds you well. I have been thoroughly engrossed in the intricacies of your project and commend the strides you have made in the efficient inference of Mixtral-8x7B models. The combination of mixed quantization with HQQ and the MoE offloading strategy is indeed a testament to the innovative spirit of your team.

Having perused your technical report and the repository with great interest, I am particularly intrigued by the prospect of speculative expert prefetching. This feature, as mentioned in the "Work in progress" section, promises to further optimise the inference process by potentially reducing the latency associated with expert loading times.

I am writing to inquire about the theoretical underpinnings and the practical considerations that are guiding the development of this feature. Specifically, I am curious about the following aspects:

1. The criteria used to determine which experts to prefetch, considering the dynamic nature of token dependencies.
2. The impact of speculative prefetching on the overall memory footprint, given the limited GPU memory and the need to balance between the LRU cache size and the prefetching mechanism.
3. The strategies in place to mitigate the overhead that may arise from prefetching experts that are not subsequently utilised within the inference process.

Furthermore, I would be interested to know if there are any preliminary results or simulations that shed light on the expected performance improvements from incorporating speculative prefetching. Such insights would be invaluable for those of us keenly following the project and considering contributing to its development.

I appreciate the open-source ethos that underpins your work and the invitation for contributions. It is my hope that by engaging in this dialogue, we can collectively enhance the functionality and robustness of the Mixtral offloading framework.

Thank you for your dedication to advancing the state of the art in model inference. I eagerly await your response and the continued evolution of this exciting project.

Best regards,
yihong1120