Continuous Batching: How to Keep Your GPU Actually Busy

2026-06-18 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

The article introduces continuous batching, a technique designed to significantly improve GPU utilization and throughput for Large Language Model (LLM) inference. It contrasts this with static batching, which suffers from the "straggler problem" where faster requests wait for the slowest, leading to 20-30% GPU utilization. Continuous batching dynamically re-evaluates the batch at every iteration, immediately freeing slots for new requests as others complete. This method, first introduced by the Orca paper in 2022, can yield up to 36x throughput improvements. It works effectively with PagedAttention, which efficiently manages the growing Key-Value (KV) cache memory by allocating fixed-size blocks on demand, preventing memory waste or crashes. While increasing throughput, continuous batching involves a tradeoff with individual request latency, requiring empirical tuning based on the specific use case. It is now a standard feature in modern inference systems like vLLM, TensorRT-LLM, and SGLang.

Key takeaway

For MLOps Engineers optimizing LLM serving infrastructure, implementing continuous batching is crucial to maximize GPU utilization and throughput. If your system currently uses static batching, you are likely leaving significant GPU capacity idle, potentially 70-80%. Transitioning to continuous batching, ideally combined with PagedAttention, will dramatically increase the number of concurrent users your system can handle, directly impacting cost-efficiency and scalability. Empirically tune your batch size to balance throughput gains against acceptable individual request latency for your specific application.

Key insights

Continuous batching dynamically optimizes LLM inference throughput by keeping GPUs fully utilized through iteration-level scheduling.

Principles

• Dynamic batch re-evaluation maximizes GPU utilization.
• Straggler requests cause significant GPU underutilization.
• Throughput and latency are often inversely related.

Method

Continuous batching re-evaluates the batch at every forward pass, immediately replacing finished requests with new ones from the queue, provided sufficient KV cache memory is available.

In practice

• Integrate with PagedAttention for KV cache efficiency.
• Tune batch size based on latency tolerance.
• Use vLLM or TensorRT-LLM for implementation.

Topics

• Continuous Batching
• LLM Inference
• GPU Utilization
• PagedAttention
• KV Cache
• Throughput Optimization
• vLLM

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

Related on AIssential

• Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient — Continuous batching optimizes LLM inference by dynamically scheduling requests and packing tokens to eliminate padding and idle GPU time.
• When GPU Utilization Lies: The Hidden Systems Problem Slowing Modern AI — GPU utilization alone is insufficient; I/O bottlenecks and resource fragmentation significantly reduce GenAI system productivity and increase costs.
• Characterizing Software Aging in GPU-Based LLM Serving Systems — GPU-based LLM serving systems exhibit statistically significant memory aging, with leak rates varying by runtime and configuration.
• Towards Multi-Model LLM Schedulers: Empirical Insights into Offloading and Preemption — Multi-model LLM serving requires schedulers to account for non-linear offloading sensitivity and high preemption costs across diverse models and hardware.
• A Coding Implementation on kvcached for Elastic KV Cache Memory, Bursty LLM Serving, and Multi-Model GPU Sharing — kvcached dynamically allocates KV-cache memory, optimizing GPU utilization for bursty LLM workloads.
• Unlock Massive Token Throughput with GPU Fractioning in NVIDIA Run:ai — Fractional GPU allocation significantly boosts LLM inference capacity and efficiency without sacrificing latency.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.