Beyond Static Batches: How Continuous Batching Redefines LLM Inference Economics

The fundamental bottleneck in early LLM serving infrastructure lay in 'static batching.' In this traditional approach, the inference engine waits to accumulate a batch of requests before processing them simultaneously. However, because user prompts and generated responses vary wildly in length, the engine must pad all sequences to match the length of the longest request in the batch. This results in 'padding waste,' where GPU tensor cores perform calculations on placeholder data rather than meaningful tokens.

Continuous batching eliminates this inefficiency through a mechanism known as 'ragged batching.' According to the Intelligence Brief: Unpacking Continuous Batching for LLM Scale, rather than padding inputs to a uniform length, the system "concatenates tokens from multiple requests sequentially using attention masks to prevent interference". This allows the GPU to process a dense stream of data, effectively removing the gaps that previously plagued static batches. When a specific request in the batch completes its generation, it is immediately ejected, and a new request is inserted into the pipeline, ensuring the hardware remains saturated with active work.

The Mechanics of Throughput

To achieve this fluid scheduling, continuous batching relies on two supporting technologies: KV Caching and Chunked Prefill.

KV Caching addresses the computational redundancy inherent in autoregressive token generation. Without it, the model would need to re-process the entire history of a conversation to generate each new word. As described in vLLM documentation, the system "stores previously calculated Key-Value pairs so that generating a new token does not require re-computing the relationships for the entire sequence history". This dramatically lowers the compute cost per token, but it introduces a memory management challenge, as the cache grows linearly with sequence length.

Chunked Prefill resolves the friction between limited GPU memory (VRAM) and long-context prompts. In standard setups, a massive prompt might exceed the memory available for a single pass. Ray Serve architecture notes explain that chunked prefill "splits input processing into batches while maintaining context via KV cache", allowing the system to ingest long documents or complex instructions without triggering Out-Of-Memory (OOM) errors. This technique ensures that the prefill phase—the initial processing of the user's prompt—does not block the generation phase of other concurrent requests.

Architectural Complexity and Adoption

The shift to continuous batching represents more than a logic optimization. NVIDIA TensorRT-LLM technical specifications describe this shift as an "architectural innovation" involving complex scheduling. Consequently, performance relies heavily on the evolution of "cache management and scheduling strategies", moving the complexity from the model layer to the serving infrastructure layer.

Prominent serving frameworks have rapidly adopted these techniques to compete with proprietary solutions like OpenAI's infrastructure. Tools such as vLLM (pioneers of PagedAttention), NVIDIA TensorRT-LLM, Ray Serve, and DeepSpeed-MII have integrated variations of continuous batching. These platforms are essential for organizations attempting to run open-weights models like Llama 3 or Mixtral at a cost-per-token that is viable for commercial applications.

While the industry has coalesced around this architecture, gaps remain in understanding the precise latency trade-offs. Specifically, the impact of chunked prefill on the 'Time to First Token' (TTFT) and the specific hardware constraints required for efficient ragged batching execution remain areas for ongoing benchmarking. Nevertheless, for high-volume inference, the era of static padding is effectively over.