The "Scheduling Art" of Modern AI Inference: From Static Batching to Continuous Batching

In production environments for Large Language Models (LLMs), inference cost is not directly determined by the number of model parameters, but rather by a core metric: Throughput.

If you are developing an AI service, you will find that the simplest inference method is "Static Batching": packing four user requests together and returning them all at once only after every request has finished generating. However, this approach has a fatal flaw in practical applications—the "Bucket Effect" (or weakest link problem).

The Pain Point of Static Batching: Waiting for the Slowest Token

LLM text generation is autoregressive, meaning the number of tokens generated per request is unpredictable. Suppose a batch contains three requests:
- Request A: Finishes after generating 10 tokens.
- Request B: Finishes after generating 50 tokens.
- Request C: Finishes after generating 500 tokens.

In static batching, even if Requests A and B complete early, GPU resources must remain occupied until Request C finishes. This means the GPU positions handling A and B experience significant "idle bubbles," drastically wasting computational resources.

The Alchemy of Continuous Batching

To solve this problem, modern inference frameworks (such as vLLM and TensorRT-LLM) have introduced Continuous Batching. Its core logic refines the scheduling granularity from the "request level" down to the "token level."

In continuous batching, the inference engine no longer waits for the entire batch to complete. Instead, it checks the status immediately after each iteration step:
1. Immediate Release: If Request A generates <|endoftext|> at step 10, it is immediately removed from the batch and returned to the user.
2. Immediate Fill: As soon as A leaves, the scheduler pulls a new Request D from the waiting queue into that slot.
3. Dynamic Alignment: At each computation step, the GPU performs matrix operations only on the currently active requests.

This mechanism pushes GPU utilization to the extreme, allowing the system to maintain low-latency responses even under high concurrency.

Understanding PagedAttention Through Memory Management

While continuous batching solves computational scheduling issues, it introduces a new challenge: memory fragmentation. Since the length of each request varies and changes dynamically, traditional contiguous memory allocation leads to severe internal fragmentation.

This is why vLLM introduced PagedAttention. It borrows the paging mechanism from operating system virtual memory:
- Split the KV Cache into fixed-size "blocks."
- Use a mapping table to record the correspondence between physical blocks and logical sequences.
- When more tokens are needed, dynamically allocate a new physical block without having to move all previous data in memory.

Practical Insights: How to Choose an Inference Strategy?

For developers, understanding these underlying mechanisms helps optimize deployment strategies:
- Pursuing Maximum Throughput $\rightarrow$ Continuous Batching + PagedAttention: Suitable for high-concurrency Chatbot services.
- Pursuing Ultra-Low Time-to-First-Token (TTFT) $\rightarrow$ Speculative Decoding: Use a small model to predict tokens preliminarily, with the large model verifying them in parallel, further reducing latency.
- Limited VRAM $\rightarrow$ Quantization + KV Cache Compression: Reduce the size of the KV Cache through FP8 or INT4 quantization, thereby accommodating more concurrent requests on the same GPU.

The evolution path of AI inference is clear: from purely pursuing computational power $\rightarrow$ to optimizing memory bandwidth $\rightarrow$ to refined scheduling management. In this process, "reducing waste" often brings more significant performance leaps than "increasing computational power."