Introducing Batch Processing for ZeroGPU

ZeroGPU has launched a Batch Processing API for asynchronous AI workloads, allowing developers to upload JSONL files for batch jobs and retrieve results upon completion. The new feature supports large-scale tasks such as data enrichment, classification, and content moderation, with endpoints for file upload, batch creation, status checks, and result downloads. The API is wire-compatible with OpenAI's batch workflow while using ZeroGPU's authentication headers, enabling integration into existing backend systems without managing queues or GPU infrastructure.

Running AI inference one request at a time works well for real-time product experiences. But many workloads do not need an immediate response. Data enrichment, classification, extraction, content moderation, summarization, and offline analytics often involve hundreds or thousands of requests that can be processed asynchronously. That is where the ZeroGPU Batch API comes in. With Batch Processing, you can upload a JSONL file, submit it as a batch job, and retrieve the results when processing is complete. It is designed for large asynchronous workloads where throughput, reliability, and simplicity matter more than instant response time. Why Batch Processing? Many AI workflows are naturally asynchronous. For example, you might want to: Sending each request individually can add unnecessary orchestration complexity. You need retry logic, request tracking, output matching, rate management, and failure handling. The Batch API gives you a cleaner workflow. How It Works Batch Processing in ZeroGPU follows a simple file-based flow: Each line in the JSONL file represents one request. ZeroGPU processes those requests asynchronously and writes the results back to output files. A minimal input line looks like this: {“custom id”:”request-1",”method”:”POST”,”url”:”/v1/chat/completions”,”body”:{“model”:”your-model-id”,”messages”: {“role”:”user”,”content”:”Classify this text.”} }} The custom id is returned in the output, so you can match every result back to your original input. Built For AI Workloads At Scale The Batch API is especially useful when you need to process a large amount of data without holding open client connections or building your own job orchestration layer. ZeroGPU currently supports batch jobs for /v1/chat/completions, with JSONL files uploaded through /v1/files. The core endpoints are: POST /v1/files to upload input JSONL. POST /v1/batches to create a batch job. GET /v1/batches/{batch id} to check status. GET /v1/files/{file id}/content to download results. This makes batch processing easy to integrate into existing backend systems, cron jobs, data pipelines, and internal tools. OpenAI-Compatible Shape ZeroGPU’s Batch and Files APIs are wire-compatible with the OpenAI-style batch workflow, while using ZeroGPU authentication headers: x-api-key: your-api-key x-project-id: your-project-id That means developers familiar with OpenAI batch jobs should feel at home, while still getting ZeroGPU’s routing, project isolation, logging, and model infrastructure. When Should You Use Batch? Use the real-time API when your user is waiting for a response. Use the Batch API when the work can happen in the background. Good fits include: Batch jobs are also easier to audit because each request has a stable custom id, and outputs are written to downloadable files. Get Started The fastest way to try it: You can try the new interactive playgrounds in the ZeroGPU docs: Upload file: /api-reference/batch/upload-file Create batch: /api-reference/batch/create-batch Retrieve batch: /api-reference/batch/retrieve-batch Download file: /api-reference/batch/download-file Batch Processing makes it easier to run AI workloads at scale without managing queues, workers, retries, or GPU infrastructure. ZeroGPU handles the execution. You focus on the data.