Batch Inference
Batch inference is the process of collecting multiple AI requests and processing them together as a group, rather than handling each individually and immediately. Instead of sending one prompt at a time and waiting for synchronous responses, batch inference queues inputs, bundles them into groups, and processes them collectively through the model — contrasting directly with real-time inference where each request receives immediate response.
The economic advantages are substantial: AI providers like Anthropic and OpenAI offer batch APIs that are 50–75% cheaper than synchronous counterparts. Cost reduction stems from superior GPU utilization — rather than processing small requests sequentially, batching allows available compute capacity to be fully utilized. NVIDIA's Tensor Cores and Blackwell architecture are specifically designed for high-throughput batch workloads.
Typical batch inference use cases: bulk document translation, automated SEO analysis of large content libraries, daily news feed summaries, product catalog classification and tagging, customer feedback sentiment analysis, and nightly analytics data processing. These scenarios share one characteristic: results are not needed in real time — delays of minutes to hours are acceptable.
Key technical parameters include batch size (number of requests per batch), maximum acceptable latency (deadline for results), error handling strategies (how to handle individual failed items within a batch), and adaptive batching (dynamically adjusting batch size based on load, token count per request, and available memory). Modern batch systems implement continuous batching for maximum GPU efficiency.