Optimizing LLM Inference Throughput
Based on the provided scenario, propose a specific architectural change to the inference system that would address the described underutilization and idle time. Explain precisely how your proposed change would lead to better hardware utilization and increased user throughput.
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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An LLM inference system is designed with two specialized hardware engines operating in a pipeline. Engine A processes the initial prompts for a batch of user requests to generate their internal state. This state is then passed to Engine B, which handles the step-by-step generation of the response tokens for that same batch. As soon as Engine A finishes with the first batch, it immediately begins processing the initial prompts for a second, new batch of requests while Engine B is still generating tokens for the first batch. What is the primary computational advantage of this two-engine architecture?
Optimizing LLM Inference Throughput
Example of Pipelined Prefilling and Decoding with Two Engines
Pipelined Engine Efficiency