Google

In large-scale, multi-user LLM serving systems, there is a fundamental conflict between maximizing system throughput and minimizing per-request latency. Strategies aimed at increasing throughput, such as batching multiple requests to process more tokens simultaneously, inherently increase the waiting time and thus the latency for individual users, particularly for short, interactive queries. Conversely, optimizing for low latency by serving requests individually or in small batches results in the underutilization of hardware resources and a reduction in overall system throughput. The ideal balance between these competing goals is contingent on the specific quality-of-service (QoS) requirements and user interaction patterns of the application.

Throughput-Latency Trade-off in LLM Inference

The choice of batch size creates a direct trade-off between system throughput and latency. Smaller batch sizes result in lower latency because fewer tokens are processed in one inference pass. This approach, however, leads to underutilized parallel computing resources, such as GPUs remaining idle, which diminishes overall system throughput. In contrast, larger batch sizes maximize throughput by fully engaging the hardware's parallelism, allowing GPUs to be occupied with large-scale matrix computations. This efficiency gain comes with higher latency, as results are only available after the entire batch, including the final token, has been processed.

Impact of Batch Size on the Throughput-Latency Trade-off

A company uses a single, powerful computing cluster to serve a large language model for two distinct applications. Analyze how the request processing strategy should differ between these two applications to achieve optimal performance for each. Justify your reasoning based on the inherent conflict between processing speed and response time.

Optimizing LLM Serving for Different Applications

In the context of a system serving many users with a large language model, explain why a strategy designed to maximize the total number of requests processed per minute often results in a longer wait time for each individual user. Describe the core conflict between the two performance metrics involved.

The Core Trade-off in LLM Serving

An engineering team is optimizing a system that serves a large language model to multiple users. To maximize the number of requests processed per hour, they decide to group incoming requests into large batches before sending them to the hardware for processing. This approach significantly increases the system's overall processing capacity. For which of the following applications would this optimization strategy be most detrimental to the user experience?

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