Pipelined Engine Efficiency
An LLM inference system uses two separate engines in a pipeline: Engine 1 for processing initial prompts (prefilling) and Engine 2 for generating subsequent tokens (decoding). When a continuous stream of request batches arrives, explain precisely when Engine 1 can start processing a new batch (e.g., Batch B) in relation to the processing of the previous batch (Batch A). Why is this timing crucial for maximizing hardware utilization?
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
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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