Batch inference
Overview
Batch inference is an operational pattern in which multiple independent inference requests are grouped and processed together by a model, rather than handled sequentially or individually. This approach leverages the vectorized computation capabilities of modern accelerators (GPUs, TPUs, specialized inference processors) to amortize fixed overhead costs across multiple requests and achieve higher throughput.
The primary motivation for batch inference is economic and performance-driven. Individual inference requests often fail to saturate the computational capacity of an accelerator; by combining requests, practitioners can drive higher GPU utilization rates, reduce cost-per-token or cost-per-request, and improve aggregate throughput. This is particularly important in production inference infrastructure where latency budgets permit delayed processing of non-real-time requests.
Batch inference introduces a trade-off between latency and throughput. While processing a single request alone may incur lower latency, grouping requests increases latency for individual items but reduces amortized cost and improves system-level efficiency. The optimal batch size depends on model architecture, hardware constraints, context window usage patterns, and memory availability.
How it works
Batch inference operates by:
- Request accumulation: Incoming inference requests are buffered until a configurable batch size is reached or a time threshold expires (to prevent unbounded latency).
- Padding and alignment: Variable-length sequences are padded to a common maximum length within the batch, or requests are grouped by similar input sizes to minimize wasted computation.
- Parallel computation: The batched inputs are passed through the model's forward pass as a single tensor operation, allowing the accelerator to vectorize computation across batch elements.
- Output unpacking: Results are segregated and returned to individual callers.
The computational gain arises because modern accelerators (especially GPUs) are optimized for high-dimensional tensor operations. A single forward pass over a batch of size n is significantly more efficient per-element than n sequential forward passes, due to reduced kernel launch overhead, better memory locality, and full utilization of parallel hardware.
Effective batch inference also requires careful management of context and memory. If input sequences are long or batch size is large, the combined memory footprint may exceed available VRAM, forcing smaller batches. Techniques such as prompt caching or chunking can mitigate memory pressure by reusing computations across requests.
| Term | Distinction |
|---|---|
| Throughput vs Latency | Batch inference trades individual request latency (slower per-request) for higher aggregate throughput (more requests per second system-wide). Latency is the time for one request; throughput is total requests completed per unit time. |
| Prompt caching | Caching stores intermediate computations to avoid redundant work within or across requests; batching groups requests to parallelize work. They are complementary techniques. |
| Inference infrastructure | Batch inference is a scheduling and execution strategy; inference infrastructure encompasses the broader platform (hardware, software, serving framework) that may or may not employ batching. |
| Fine-tuning | Fine-tuning adapts model weights to specific tasks; batch inference is an operational execution pattern applied after the model is frozen, irrespective of whether it was fine-tuned. |
| Sequential inference | Sequential inference processes one request at a time through the model. Batch inference processes multiple requests in parallel, achieving higher throughput but potentially higher per-request latency. |
Examples
- Large-scale text generation services: Providers such as OpenAI, Anthropic, and Cohere employ batch inference for non-real-time workloads (e.g., batch generation APIs). Users submit thousands of prompts; the service accumulates them and processes them in large batches (often 100–1000 per batch) to maximize GPU utilization and reduce cost per output token.
- LLM-based retrieval and ranking pipelines: Systems that use an embedding model to encode many documents or use a reranker to score multiple candidate passages batch hundreds or thousands of texts in a single forward pass, reducing end-to-end latency compared to scoring each passage individually.
- Batch evaluation: Automated evaluation frameworks that compute BLEU, ROUGE, or perplexity across a golden dataset of hundreds of examples use batch inference to score all examples in a single pass, completing evaluation in minutes rather than hours.
See also
- Inference infrastructure – The hardware and software foundation on which batch inference runs
- Latency vs throughput (LLM) – The fundamental trade-off managed by batch inference
- Prompt caching – A complementary technique to reduce redundant computation
- Context window – A constraint that affects batch size selection
- LLM Optimization – Broader category encompassing batch inference and other efficiency improvements
