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How to Process Large Datasets with LLMs Using Batch Inference

Yammbo
· 3 min read
Copyright: CC BY-NC-SA 4.0
large dataset processing ai model efficiency cost-effective ai jsonl data format api batch processing
How to Process Large Datasets with LLMs Using Batch Inference

Processing large volumes of data with Large Language Models (LLMs) can be a significant challenge. When you need to classify, summarize, or extract entities from hundreds of thousands or even millions of records, sending individual requests to a serverless API can quickly become costly and slow. This tutorial introduces batch inference, a powerful and cost-effective method for handling such large-scale LLM tasks. You'll learn how to prepare your data, submit batch jobs, monitor their progress, and retrieve results efficiently.

Step 1: Understanding Batch Inference for Large Language Models

Batch inference is a method of processing a large collection of inputs (a batch) with an AI model in a single, asynchronous job, rather than sending each input as an individual, real-time request. For LLMs, this means bundling thousands of prompts and their associated data into one submission. The primary advantage of batch inference lies in its efficiency and cost-effectiveness for high-volume tasks.

Unlike serverless inference, where each request incurs overhead and is processed individually, batch jobs are optimized for throughput. API providers can schedule these jobs during off-peak times or allocate resources more efficiently, leading to significantly lower per-token costs. This makes batch processing ideal for tasks like:

  • Classifying extensive customer feedback or support tickets.
  • Summarizing large archives of documents or articles.
  • Extracting specific entities from vast text corpora.
  • Performing sentiment analysis on massive datasets of social media posts.

The core concept remains similar to serverless requests: you prepare a prompt and context for the LLM. The key difference is how these requests are packaged and submitted. Instead of an immediate response, you get a job ID, which you then use to monitor the processing and retrieve the results once the entire batch is complete.

Verification: You should now have a clear understanding of when and why batch inference is a superior choice for large-scale LLM processing compared to traditional serverless API calls.

Step 2: Preparing Your Data in JSONL Format

The standard format for submitting batch inference jobs to many API providers is JSON Lines (JSONL). A JSONL file consists of multiple JSON objects, with each object placed on its own line. Each line in your JSONL file represents a single, independent request to the LLM. This means that if you have 250,000 records to process, your JSONL file will contain 250,000 lines, each a valid JSON object.

Each JSON object should contain the payload you would typically send in a single API request. For example, if your serverless API call expects a prompt field, then each line in your JSONL file will be a JSON object with a prompt key and its corresponding value. You can also include other parameters that your specific LLM API supports, such as temperature, max_tokens, or model, within each JSON object.

Here's an example of what a JSONL file might look like for a text classification task:

{