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DéjàVu: KV-cache Streaming for Fast, Fault-tolerant Generative LLM Serving

Core Concepts
D´ej`aVu proposes efficient solutions to challenges in large-scale LLM serving through KV cache streaming, disaggregation, and fault tolerance mechanisms.
The paper introduces DéjàVu, a system addressing challenges in distributed LLM serving. It focuses on prompt-token disaggregation, microbatch swapping for GPU memory management, and fault tolerance through KV cache replication. The system aims to improve throughput and reduce latency in large model deployments. The key challenges identified include latency discrepancies between prompt processing and token generation leading to pipeline bubbles, inefficient GPU memory usage due to overprovisioning of the KV cache, and lack of efficient failure handling mechanisms. DéjàVu addresses these challenges by optimizing resource allocation, implementing microbatch swapping for GPU memory efficiency, and replicating KV cache state for fault tolerance. DéjàVu's approach involves using a versatile KV cache streaming library (DéjàVuLib) to enable fast streaming for diverse configurations. The system is evaluated under various scenarios showing improvements in throughput and latency compared to existing systems like FasterTransformer.
Figure 1 shows the GPU memory footprint required for serving various LLMs with different sequence lengths. D´ej`aVu improves LLM serving throughput by up to 2× compared to FasterTransformer in pipeline parallel setups. Microbatch swapping can improve throughput by up to 1.8× by accommodating larger batch sizes. D´ej`aVu reduces microbatch latency by 1.54× compared to non-fault-tolerant systems in the presence of failures.
"We propose D´ejaVu, an efficient and fault-tolerant LLM serving system based on KV cache streaming." "D´ejaVu aims to address challenges such as bubbles in pipeline parallel deployments caused by latency discrepancies." "Our approach involves disaggregating prompt processing from token generation and optimizing resource allocation."

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by Foteini Stra... at 03-05-2024

Deeper Inquiries

How does DéjàVu's approach compare with other existing systems for large-scale LLM serving

DéjàVu's approach to large-scale LLM serving stands out in comparison to other existing systems due to its comprehensive solutions addressing key challenges in stateful, distributed inference. While systems like FasterTransformer and vLLM focus on optimizing memory allocation and dynamic cache management, DéjàVu goes a step further by introducing efficient prompt-token disaggregation, microbatch swapping for GPU memory management, and fault-tolerant mechanisms using KV cache replication. This holistic approach allows DéjàVu to address issues such as bubbles in pipeline parallel setups, GPU memory overprovisioning, and system failures more effectively than other systems.

What are the potential drawbacks or limitations of implementing microbatch swapping in GPU memory management

While microbatch swapping offers benefits like reducing the amount of GPU memory required for the KV cache and increasing system throughput by accommodating larger batch sizes, there are potential drawbacks or limitations associated with its implementation. One limitation is the overhead introduced by transferring the KV cache between CPU and GPU during swapping operations. Depending on factors like the size of the cache and PCIe bandwidth constraints, this transfer process can introduce latency that may impact overall system performance. Additionally, managing multiple simultaneous swaps efficiently without causing bottlenecks or resource contention can be challenging.

How can the concept of fault tolerance in LLM serving be applied to other machine learning models or applications

The concept of fault tolerance in LLM serving can be applied to other machine learning models or applications by implementing robust strategies for handling failures gracefully without compromising service quality. By incorporating mechanisms like KV cache replication for fast recovery from failures, proactive monitoring for detecting faults early on, and seamless resumption of interrupted processes at the point of failure can enhance reliability across various ML applications. These fault-tolerant approaches not only ensure uninterrupted service but also contribute to improved user experience and operational efficiency in critical scenarios where downtime is not an option.