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Aggregated Architecture for Prefilling and Decoding
An architectural model where the prefilling and decoding phases of inference are treated as separate stages of computation but are executed on the same hardware. This approach is a common foundation for advanced batching techniques that improve upon simpler strategies.
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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
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Aggregated Architecture for Prefilling and Decoding
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?
Analyzing LLM Serving Strategies
Efficiency of Sequential vs. Batched Processing
Throughput-Latency Trade-off in LLM Inference
Simultaneous Token Generation in Batched Decoding
Sequence Concatenation in Disaggregated Inference
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Continuous Batching for LLM Inference
In a common architecture for language model inference, the initial processing of a user's prompt (prefilling) and the subsequent token-by-token generation of the response (decoding) are treated as distinct computational stages, even though they execute on the same hardware. What is the primary analytical reason for this architectural separation?
Optimizing Inference Throughput
Trade-offs in a Staged Inference Architecture