Batch Inference
Definition
Batch inference processes inputs collected over time (hours, days) in a single efficient run, taking advantage of GPU utilization patterns that minimize overhead versus processing items one-by-one. Common batch inference use cases include overnight content classification, weekly customer churn scoring, bulk document summarization, and embedding generation for vector database population. Batch jobs are orchestrated by workflow schedulers like Apache Airflow, Prefect, or AWS Batch, which provision compute on demand and release it after the job completes.
Why It Matters
Batch inference dramatically reduces AI compute costs for non-latency-sensitive workloads. Real-time online inference maintains always-on server infrastructure for low-latency responses; batch inference provisions resources only when needed and can use spot or preemptible instances at 60-80% cost savings. For tasks like generating weekly performance reports, enriching CRM data with AI insights, or pre-computing search embeddings, batch inference provides the same model quality at a fraction of the cost of maintaining a live API.
How It Works
A batch inference job reads input records from a data store (S3, database, message queue), batches them into groups sized to maximize GPU utilization (commonly 32-256 items depending on input length and model size), runs model inference on each batch in sequence, and writes results back to an output store. Parallelism across multiple GPU workers can be added for large jobs. Progress tracking, checkpointing, and automatic retry on failure ensure that large jobs complete reliably even if interrupted.
Batch Inference Pipeline
1
Collect Requests
Queue up N inputs
2
Form Batch
Group by size / deadline
3
GPU Inference
Process batch in parallel
4
Distribute Results
Route outputs back to callers
GPU utilization: 90%+ vs 20-30% for individual requests
Real-World Example
An e-commerce company generates product descriptions using an LLM for their 50,000-product catalog. Rather than serving these in real time (which would require 50,000 live API calls from their website), they run a nightly batch inference job on AWS Batch using a single GPU instance. The job processes all products in 45 minutes, costs $3.20 in compute, and writes descriptions to their database — enabling instant page loads without per-request LLM costs.
Common Mistakes
- ✕Using real-time inference infrastructure for batch workloads, paying for always-on servers when compute is only needed a few hours per day
- ✕Not implementing checkpointing for large batch jobs — a failure 80% through a 24-hour job forces starting over from scratch
- ✕Ignoring GPU memory management in batch loops — accumulating tensors in memory over thousands of iterations causes OOM failures on large batches
Related Terms
Online Inference
Online inference (also called real-time inference) is the processing of individual or small groups of model inputs immediately upon arrival, returning results within milliseconds to seconds to support interactive applications like chatbots, search, and recommendations.
Inference Latency
Inference latency is the time between submitting an input to a deployed AI model and receiving the complete output — typically measured in milliseconds for classification models and seconds for large language models — directly impacting user experience and system design.
Model Serving
Model serving is the infrastructure that hosts trained ML models and exposes them as APIs, handling prediction requests in production with the latency, throughput, and reliability requirements of real applications.
AI Cost Optimization
AI cost optimization encompasses techniques to reduce the compute, storage, and API expenses of AI systems—through model selection, caching, batching, quantization, and architecture decisions—making AI economically sustainable at scale.
Data Pipeline
A data pipeline is an automated sequence of data collection, processing, transformation, and loading steps that delivers clean, structured data from sources to destinations—forming the foundation of every ML training and serving system.
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