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Batching in LLM inference is a technique where multiple input sequences are processed simultaneously as a single group, or batch, instead of individually. This method is highly effective because it leverages the parallel processing capabilities of modern GPUs. By computing multiple sequences in a single forward pass, batching ensures that the hardware is fully utilized, making it a crucial strategy for efficiently serving large language models at scale.

Batching in LLM Inference

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.

Aggregated Architecture for Prefilling and Decoding

Static batching is a scheduling strategy where once a batch of requests is sent for execution, its processing is uninterruptible. The scheduler must wait for the entire batch to be fully processed before it can assemble and dispatch the next one.

Static Batching

A development team is deploying a large language model for a text summarization service. They are testing two different serving strategies. Analyze the two strategies described in the case study and explain why System B achieves higher hardware utilization and overall request throughput.

Analyzing LLM Serving Strategies

An LLM inference server receives two user requests simultaneously. Compare the computational efficiency of processing these requests sequentially (one after the other) versus processing them as a single batch. In your explanation, focus on how the use of parallel processing hardware, like GPUs, is affected in each scenario.

Efficiency of Sequential vs. Batched Processing

In large-scale, multi-user LLM serving systems, there is a fundamental conflict between maximizing system throughput and minimizing per-request latency. Strategies aimed at increasing throughput, such as batching multiple requests to process more tokens simultaneously, inherently increase the waiting time and thus the latency for individual users, particularly for short, interactive queries. Conversely, optimizing for low latency by serving requests individually or in small batches results in the underutilization of hardware resources and a reduction in overall system throughput. The ideal balance between these competing goals is contingent on the specific quality-of-service (QoS) requirements and user interaction patterns of the application.

Throughput-Latency Trade-off in LLM Inference

During the decoding phase of a batched inference process, a Large Language Model generates tokens simultaneously for all the sequences within the batch. This generation process continues until the token generation for the longest sequence in the batch reaches completion.

Simultaneous Token Generation in Batched Decoding

When prefilling and decoding are disaggregated during Large Language Model inference, multiple short sequences can be concatenated into a single long sequence for joint processing. This strategy maximizes the number of tokens processed in a batch, improving the throughput of the prefilling phase while minimizing the need for padding tokens. As a trade-off, this approach introduces additional communication overhead because the Key-Value (KV) cache must be transferred to the decoding devices. Consequently, achieving optimal performance with this method typically requires a high-bandwidth, low-latency network.

Sequence Concatenation in Disaggregated Inference

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?

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