In-depth Analysis of Memory Management for Enhanced Performance on Consumer-grade GPUs

[yihong1120]

Dear PowerInfer Contributors,

I hope this message finds you well. I am reaching out to discuss a potential enhancement to the PowerInfer inference engine, specifically regarding the memory management strategies employed during LLM inference on consumer-grade GPUs.

Upon a thorough examination of the current implementation, I have observed that while the engine adeptly handles the distribution of workload between the CPU and GPU, there may be room for optimisation in the way memory is allocated and managed, particularly during peak usage scenarios.

The crux of the matter lies in the dynamic allocation of memory for 'hot' and 'cold' neurons. While the preloading of 'hot' neurons onto the GPU is commendable for its efficiency, the allocation of memory for 'cold' neurons during runtime could potentially be streamlined. This is especially pertinent when considering the limited VRAM available on consumer-grade GPUs compared to their server-grade counterparts.

I propose a more granular control over memory allocation, which could include:

• Implementing a more sophisticated memory pooling mechanism to reduce fragmentation and improve allocation speed.
• Exploring the use of memory compression techniques to increase the effective capacity of VRAM.
• Introducing a dynamic memory re-allocation system that can adapt to the changing patterns of 'hot' and 'cold' neuron activations based on real-time usage.

I believe that by addressing these aspects, PowerInfer could achieve even greater performance gains and efficiency, making it more accessible and practical for a wider range of users.

I would be most interested in hearing your thoughts on this matter and am keen to contribute to the development of such enhancements.

Thank you for your time and consideration.

Best regards, yihong1120

[hodlen]

Hello @yihong1120,

Thank you for your thorough analysis and for recognising the potential in our architecture and promising plans. We truly appreciate your attention to detail and the time you've invested in studying PowerInfer.

We're excited about your interest in contributing and would love to hear more about your ideas for improvement. If you could provide a detailed design on each aspect and even contribute directly to our codebase, that would be fantastic.

If you want to implement these improvements, we suggest tackling them step-by-step, starting with more isolated features such as memory pooling to reduce fragmentation. Given the complexity of VRAM compression and dynamic re-allocation, these might require a more extensive engineering effort.

Once again, thank you for your valuable analysis, suggestions, and willingness to collaborate. We warmly welcome you to keep in touch with us and look forward to seeing your PR!

Best regards, PowerInfer Dev Team