Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient

2026-05-30 · Source: MachineLearningMastery.com - Machinelearningmastery.com · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, extended

Summary

Continuous batching significantly enhances Large Language Model (LLM) inference efficiency by addressing the limitations of static batching, which wastes GPU cycles on padding tokens and forces short requests to wait for longer ones. This technique combines dynamic scheduling, which immediately frees GPU slots upon sequence completion to admit new prompts, with ragged batching. Ragged batching concatenates all in-flight tokens into a single unpadded row and employs a block-diagonal causal attention mask to isolate sequences, effectively fusing prefill and decode steps without padding. A practical demonstration with "openai-community/gpt2" showed continuous batching completing six varied requests in 9.54 seconds on an MPS device, a substantial improvement over static batching's 61.80 seconds, while maintaining identical output.

Key takeaway

For MLOps engineers deploying LLM inference servers, adopting continuous batching is crucial for maximizing GPU utilization and throughput. Your systems can achieve substantial performance improvements, as demonstrated by a 6x speedup (61.80s to 9.54s) over static batching. Implement dynamic scheduling and ragged batching to eliminate wasteful padding and ensure immediate slot reuse, significantly reducing latency for varied request lengths.

Key insights

Continuous batching optimizes LLM inference by dynamically scheduling requests and packing tokens to eliminate padding and idle GPU time.

Principles

• Static batching wastes GPU cycles on padding and idle slots.
• Dynamic scheduling improves throughput by immediate slot reuse.
• Ragged batching enables efficient token processing via concatenation and masked attention.

Method

Continuous batching employs an iteration-level scheduler to manage `waiting_queue` and `running_set`, forming batches of concatenated tokens. It runs a single model forward pass using a block-diagonal causal attention mask and updates per-sequence KV caches.

In practice

• Implement iteration-level scheduling to free GPU slots instantly.
• Use block-diagonal causal masks for packed attention.
• Concatenate prefill and decode tokens into a single forward pass.

Topics

• LLM Inference
• Continuous Batching
• Dynamic Scheduling
• Ragged Batching
• GPU Optimization
• KV Cache
• Attention Masks

Code references

• vllm-project/vllm

Best for: Machine Learning Engineer, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by MachineLearningMastery.com - Machinelearningmastery.com.