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LLM Inference Architecture with Scheduling
The architecture of a practical LLM inference system centers around a scheduler and an inference engine. The scheduler groups user requests into batches and dispatches them to the inference engine for execution. By integrating a scheduler, the system gains the flexibility to adjust batch processing dynamically, which helps in optimizing and balancing both computational throughput and response latency.
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Foundations of Large Language Models
Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Scheduler in LLM Inference Systems
Inference Engine in LLM Systems
Request Processing Workflow in LLM Inference
A team is optimizing their system for serving a large language model. They observe that during peak traffic, many user requests fail with a timeout error before the model begins processing them. At the same time, monitoring shows that the hardware responsible for the model's computations is frequently idle. Based on this scenario, which of the following actions would most directly target the likely cause of this bottleneck?
A system designed to serve a large language model is composed of distinct parts, each with a specific job. Match each component with its primary responsibility within the system.
Optimizing an LLM Inference System
LLM Inference Architecture with Scheduling