{"slug": "introducing-batch-processing-for-zerogpu", "title": "Introducing Batch Processing for ZeroGPU", "summary": "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.", "body_md": "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.\n\nThat is where the ZeroGPU Batch API comes in.\n\nWith 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.\n\nWhy Batch Processing?\n\nMany AI workflows are naturally asynchronous.\n\nFor example, you might want to:\n\nSending each request individually can add unnecessary orchestration complexity. You need retry logic, request tracking, output matching, rate management, and failure handling.\n\nThe Batch API gives you a cleaner workflow.\n\n**How It Works**\n\nBatch Processing in ZeroGPU follows a simple file-based flow:\n\nEach line in the JSONL file represents one request. ZeroGPU processes those requests asynchronously and writes the results back to output files.\n\nA minimal input line looks like this:\n\n```\n{“custom_id”:”request-1\",”method”:”POST”,”url”:”/v1/chat/completions”,”body”:{“model”:”your-model-id”,”messages”:[{“role”:”user”,”content”:”Classify this text.”}]}}\n```\n\nThe custom_id is returned in the output, so you can match every result back to your original input.\n\n**Built For AI Workloads At Scale**\n\nThe 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.\n\nZeroGPU currently supports batch jobs for /v1/chat/completions, with JSONL files uploaded through /v1/files.\n\nThe core endpoints are:\n\n```\nPOST /v1/files to upload input JSONL.\nPOST /v1/batches to create a batch job.\nGET /v1/batches/{batch_id} to check status.\nGET /v1/files/{file_id}/content to download results.\n```\n\nThis makes batch processing easy to integrate into existing backend systems, cron jobs, data pipelines, and internal tools.\n\n**OpenAI-Compatible Shape**\n\nZeroGPU’s Batch and Files APIs are wire-compatible with the OpenAI-style batch workflow, while using ZeroGPU authentication headers:\n\n```\nx-api-key: your-api-key\nx-project-id: your-project-id\n```\n\nThat means developers familiar with OpenAI batch jobs should feel at home, while still getting ZeroGPU’s routing, project isolation, logging, and model infrastructure.\n\n**When Should You Use Batch?**\n\nUse the real-time API when your user is waiting for a response.\n\nUse the Batch API when the work can happen in the background.\n\nGood fits include:\n\nBatch jobs are also easier to audit because each request has a stable custom_id, and outputs are written to downloadable files.\n\n**Get Started**\n\nThe fastest way to try it:\n\nYou can try the new interactive playgrounds in the ZeroGPU docs:\n\n```\nUpload file: /api-reference/batch/upload-file\nCreate batch: /api-reference/batch/create-batch\nRetrieve batch: /api-reference/batch/retrieve-batch\nDownload file: /api-reference/batch/download-file\n```\n\nBatch Processing makes it easier to run AI workloads at scale without managing queues, workers, retries, or GPU infrastructure.\n\nZeroGPU handles the execution. You focus on the data.", "url": "https://wpnews.pro/news/introducing-batch-processing-for-zerogpu", "canonical_source": "https://dev.to/josh_zerogpu/introducing-batch-processing-for-zerogpu-1lb1", "published_at": "2026-05-28 14:03:32+00:00", "updated_at": "2026-05-28 14:24:35.376243+00:00", "lang": "en", "topics": ["ai-infrastructure", "ai-products", "ai-tools", "artificial-intelligence", "machine-learning"], "entities": ["ZeroGPU", "Batch API"], "alternates": {"html": "https://wpnews.pro/news/introducing-batch-processing-for-zerogpu", "markdown": "https://wpnews.pro/news/introducing-batch-processing-for-zerogpu.md", "text": "https://wpnews.pro/news/introducing-batch-processing-for-zerogpu.txt", "jsonld": "https://wpnews.pro/news/introducing-batch-processing-for-zerogpu.jsonld"}}