DEEP EXPLANATION

Quantization, Distillation, and Smaller Models for Production (EXPLAINED)

Project BasedLLMsHard20 min read

Hard project question on INT8/INT4 quantization, knowledge distillation, and serving smaller models for cost and latency.

TL;DR — Quick Answer

Quantization reduces weight precision (FP16 → INT8/INT4) to cut memory and speed inference with minimal quality loss on many tasks. Distillation trains a smaller student model to mimic a larger teacher on prompts/outputs or logits. Use them when latency and cost dominate — classification, routing, embeddings, high-QPS chat tiers — not when you need maximum reasoning on hard novel tasks. Always eval on golden set after compression; combine with routing to escalate hard queries to larger models.

The Interview Question

Explain quantization, distillation, and deploying smaller models in production. When do these techniques make sense?

Deep Explanation

Quantization

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