Batching

Batch inference (Batching) combines multiple requests within a short time into a single batch, which can significantly improve GPU throughput, but introduces additional queuing latency and requires trade-offs based on latency tolerance.

Increasing batch size to improve throughput typically sacrifices per-request latency

Conversational applications tolerate latency far less than batch applications, so latency upper limits should be set according to the interaction scenario.

Among Semantic Router's improvement directions, "faster" involves caching, batching or lighter embedding models, while "scalable" and "integration with decision models" relate to how routing rules are organized and how routing results are handed to downstream decision models.

For teams building similar AI coding tools, it is worth regularly reviewing the data transmission pipeline to look for opportunities for compression and batching.

Inference optimization is the core technical direction for reducing AI's per-use cost, with common methods including model quantization (representing weights at lower precision such as INT8/FP8 to reduce memory footprint and computation), knowledge distillation (using a large model to guide a small model), KV cache reuse and batching (merging requests to improve GPU utilization), and speculative decoding (a small model drafts and a large model verifies to speed up generation).

For the single-Agent performance evaluation scenario, introducing a multi-Agent architecture for batch processing is not recommended, because it brings complexity such as memory maintenance.

The randomness of LLM inference comes from at least three independent sources: the sampling strategy (temperature, top-p), the accumulated error of floating-point operations, and changes in the reduction order of matrix multiplications caused by changes in batch size.

Batch processing suits latency-insensitive, high-throughput scenarios, and is simple to implement and lower in cost.

The core assumption of traditional batch processing is that decisions can wait, and when facing events that require immediate response, latency becomes a hard flaw.

A practical way to judge whether to introduce a real-time architecture is to look at the decision window: if minutes or more are acceptable, optimizing the batch scheduling frequency may be more cost-effective; only when the decision window is compressed to seconds and the benefits cover the extra complexity and cost is real-time analytics worth investing in.

The cost curves of continuously running computation tasks and on-demand scheduled batch tasks are completely different, and the cost of real-time systems often only becomes apparent after they run stably.

The application demands of real-time analytics are compressing the decision window from hours to seconds or even sub-seconds.

Content freshness and permission freshness do not need the same solution: one needs a faster batch job, and the other needs to stop doing batch jobs entirely.

Batch processing handles accumulated data in fixed time windows, while stream processing responds immediately as data arrives; the two differ significantly in tool selection, cost structure, and operational complexity.

For inference service providers, aggressive batching strategies can improve throughput but may increase latency for individual requests.

In Coze workflows, loop nodes are equivalent to a single machine processing items one by one, while batch processing is equivalent to multiple machines processing simultaneously—batch processing is more efficient but consumes more resources and has more restrictions.

Inputs of different lengths within a batch need to be padded to a uniform length, which introduces additional computational overhead.

The lack of batch cancellation capability means that once a task containing an erroneous prompt has been submitted, one can only wait for it to complete, wasting computational resources and API quota.

A complete batch task management lifecycle typically includes five phases: creation, querying, monitoring, cancellation, and result retrieval.