PRESERVE: Prefetching Model Weights and KV-Cache in Distributed LLM Serving (2501.08192v2)

Abstract: LLMs are typically served from clusters of GPUs/NPUs that consist of large number of devices. Unfortunately, communication between these devices incurs significant overhead, increasing the inference latency and cost while limiting the scalability. Prior work addressed this issue by overlapping communication with compute, but has severe limitations due to the data dependencies between these operations. In this paper, we propose PRESERVE, a novel framework that prefetches model weights and KV-cache from off-chip HBM memory to the on-chip cache of AI accelerators during the communication operations, which offers various advantages and performance improvements compared to prior methods. Through extensive experiments conducted on commercial AI accelerators, we demonstrate up to 1.6x end-to-end speedup on state-of-the-art, open-source LLMs. Additionally, we perform a design space exploration that identifies the optimal hardware configuration for the proposed method, showing a further 1.25x improvement in performance per cost by selecting the optimal L2 cache size. Our results show that PRESERVE has the potential to mitigate the memory bottlenecks and communication overheads, offering a solution to improve the performance and scalability of the LLM inference systems.

• The paper presents a novel framework, PRESERVE, which prefetches model weights and KV-cache to reduce LLM inference latency.
• It overlaps memory transfers with collective communication, achieving up to a 1.6x speedup on commercial AI accelerators.
• The study’s design space exploration reveals that increasing L2 cache from 8 MB to 104 MB can boost performance per cost by up to 1.25x.

A Review of "PRESERVE: Prefetching Model Weights and KV-Cache in Distributed LLM Serving"

The presented paper introduces PRESERVE, an advanced prefetching framework aimed at enhancing the efficiency of LLMs during inference on distributed AI accelerators. The work addresses significant computational challenges encountered in LLM inference, specifically focusing on memory bandwidth bottlenecks and inter-device communication latency.

Technical Insights

PRESERVE proposes a method to prefetch model weights and key-value (KV) cache from off-chip memory to on-chip cache while concurrently running collective communication operations. The framework's integration allows for overlapping memory reads with communication tasks, hence reducing end-to-end inference latency. The experimental results show notable improvements in inference speed, achieving up to a 1.6x speedup on commercial AI accelerators when benchmarked against state-of-the-art open-source LLMs.

The paper's methodological contribution is underscored by a robust design space exploration, identifying optimal hardware configurations that bolster inference performance while minimizing cost. The analysis indicates an optimal increase in L2 cache size from 8 MB to 104 MB with the proposed method, yielding a further 1.25x improvement in performance per cost.

Performance Evaluation

The experiments outlined in the paper demonstrate the efficacy of PRESERVE across various LLMs, including Llama3-8b and Llama3-70b, under different configurations of batch size and sequence length. The performance is shown to generally improve with an increase in cluster size, highlighting the impact of communication latency in multi-device setups. The paper confirms that balanced prefetch and communication latency maximizes speedup, a crucial finding for scaling inference systems efficiently.

Theoretical and Practical Implications

Theoretically, the research extends the understanding of memory-bandwidth constraints in LLM inference and presents an innovative approach to alleviate these challenges through strategic prefetching. Practically, the findings offer a pathway for optimizing AI accelerators' architecture to meet the growing demands of real-time LLM applications in a cost-effective manner.

Future Developments

Future work may include extending the framework to accommodate other memory architectures and further improving the prefetching algorithm's efficiency. There is also potential for integrating PRESERVE with other optimization strategies, such as model parallelism or mixed precision computing, to further amplify its benefits.

In summary, this paper makes a significant contribution to the domain of efficient LLM inference, providing both a novel solution and a comprehensive analysis of its integration and impacts. The insights and methodologies presented establish a foundation for further research and development in optimizing AI hardware and firmware for large-scale LLMs.