Throughput-Latency Trade-off in LLM Inference
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.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Related
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Static Batching
A technology company is optimizing its popular chatbot service, which is powered by a large language model and handles thousands of simultaneous user queries. To manage this high load, their engineers implement a system that waits to collect several user queries and processes them together as a single group in one computational step. Which of the following outcomes is the most direct and significant advantage of this approach?
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Throughput-Latency Trade-off in LLM Inference
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A financial services company is choosing between two language models for its new customer support chatbot. Both models meet the company's strict requirements for response speed, factual accuracy, and memory footprint. However, Model A requires a complex, multi-step setup process and specialized software that the company's IT team is unfamiliar with, while Model B integrates seamlessly with their existing infrastructure. Which additional dimension of inference efficiency is the most critical deciding factor in this scenario?
Throughput-Latency Trade-off in LLM Inference
Learn After
Impact of Batch Size on the Throughput-Latency Trade-off
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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The Core Trade-off in LLM Serving