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Priority-based scheduling is a general strategy for managing LLM inference by allocating system resources according to the designated importance of certain requests or computational steps. This approach aligns resource usage with specific performance goals. For instance, decoding steps can be prioritized to minimize token generation latency for individual requests, whereas prefilling steps can be prioritized to maximize overall system throughput in batch-processing scenarios.

Priority-Based Scheduling in LLM Inference

The prefilling-prioritized strategy is a core characteristic of continuous batching where the scheduler adds new requests to the active batch as soon as the inference engine has available resources. By processing these new requests for prefilling as early as possible, this approach is designed to maximize system throughput. However, this prioritization comes at the cost of increased latency for ongoing requests, as the prefilling of new, long inputs can extend the overall processing time for the entire batch.

Prefilling-Prioritized Strategy in Continuous Batching

The decoding-prioritized strategy is characteristic of standard (or static) batching, where the system must wait for every sequence in the current batch to complete generation before it can begin processing new requests. This approach ensures relatively low latency for the requests within the active batch. However, this focus on completing the current batch leads to lower overall device utilization and system throughput, as hardware can sit idle while waiting for the longest sequence to finish.

Decoding-Prioritized Strategy in Standard Batching

In practical applications, scheduling systems can be designed with custom priority policies that go beyond simple prefill/decode prioritization. These policies allow practitioners to account for specific operational needs and constraints, such as meeting request deadlines or giving precedence to requests based on user-defined importance levels.

Custom Priority Policies in LLM Scheduling

An LLM inference system is currently generating responses for several interactive user chats. A new, large batch of requests for offline document analysis arrives. The system scheduler must decide whether to immediately start processing the initial prompts for the new batch or to wait until the current chat responses are fully generated. Explain the likely impact on overall system throughput and the response time for the chat users if the scheduler chooses to immediately process the new batch.

Inference Scheduling Trade-offs

A new, long-running batch job from a standard user arrives at the same time as a short, interactive query from a premium user. Based on the principles of priority-based scheduling, which task should the system prioritize and why? Justify your answer by explaining the impact of your choice on both user types.

Optimizing a Hybrid LLM Service

An AI company operates a service that uses a large language model to summarize vast archives of legal documents. The primary business goal is to maximize the total number of documents summarized each day. The system receives a constant stream of new summarization requests. Given this primary goal, which scheduling approach for managing inference tasks would be most effective?

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