AI Glossaryinfrastructure

Inference Optimization

Techniques to make AI model predictions faster, cheaper, and more efficient in production, including quantization, batching, caching, and model distillation.

How It Works

Inference is where cost and latency hit your bottom line. Every API call costs money, and every millisecond of latency affects user experience. Inference optimization is about getting the same (or similar) quality output while spending less time and money. Key optimization techniques: (1) Quantization — reduce model precision from 32-bit to 8-bit or 4-bit floating point. Cuts memory usage and speeds inference by 2-4x with minimal quality loss. (2) Batching — process multiple requests together for better GPU utilization. (3) KV-cache optimization — reuse computed attention values for faster generation. (4) Speculative decoding — use a small, fast model to draft tokens and a large model to verify them. (5) Prompt caching — cache the processing of common prompt prefixes (Anthropic offers this natively). For API users, optimization means: choosing the right model size (use gpt-4.1-mini instead of gpt-5.2 when quality is sufficient), caching responses for repeated queries, streaming to improve perceived latency, and batching non-urgent requests. The cheapest inference call is the one you do not make — aggressive caching can cut costs by 50-80%.

Common Use Cases

  • 1Reducing API costs in production
  • 2Real-time AI features (sub-100ms)
  • 3Mobile and edge deployment
  • 4High-throughput batch processing
  • 5Scaling AI services cost-effectively

Related Terms

Batch Processing

Processing multiple AI requests together as a group, typically at lower cost and higher throughput than real-time individual requests.

Model Serving

The infrastructure and process of hosting a trained AI model and exposing it as an API endpoint for real-time or batch inference.

Quantization

A technique that reduces AI model size and memory requirements by using lower-precision numbers to represent model weights, trading a small accuracy loss for major efficiency gains.

Distillation

A technique where a smaller "student" model is trained to replicate the behavior of a larger "teacher" model, achieving comparable quality at lower cost.

Latency

The time delay between sending a request to an AI model and receiving the response, critical for real-time user-facing applications.

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