Batch Inference: High-Throughput AI
Definition: Processing predictions on multiple unlabelled examples divided into smaller subsets, leveraging accelerator chip parallelization.
Real-Time vs. Batch
- Real-Time: ChatGPT. You type, it types back. Low latency is key.
- Batch Inference: “Here is a CSV of 10,000 product descriptions. Write SEO tags for all of them by tomorrow.” Throughput is key.
The Vibe Coding Workflow
Vibe coding is usually real-time. But sometimes you need to “scale the vibe.”
- Scenario: You just vibe-coded a new “Summary” feature for your blog. Now you need to generate summaries for your 500 old posts.
- Don’t: Copy-paste 500 times into Claude.
- Do: Write a script (using the AI) to send a Batch Request to the OpenAI API.
- OpenAI’s Batch API is 50% cheaper than the real-time API. It runs within 24 hours.
Parallelization
Batch inference allows parallel processing. You can have 50 workers hitting the API simultaneously (within rate limits).
- Rate Limits: The biggest enemy of batch inference. Ask the AI to “Write a Python script with exponential backoff and retry logic” to handle 429 errors.
Expert Takeaway
Vibe coding builds the prototype (the prompt). Batch inference builds the production data. Once you perfect the prompt in the chat, graduate to a batch script.