Part 3 covered parallelism and speed techniques. This is the final part (4 of 4) of the Production LLM Inference series. It covers inference frameworks, production operations, and a practical decision framework for choosing the right stack.
Part 2 covered quantization and KV cache optimization. This is Part 3 of 4 — it focuses on the techniques that accelerate inference: multi-token prediction and distributed parallelism strategies.
Part 1 covered model architectures and the key metrics of production inference. This is Part 2 of 4 – it focuses on the two largest VRAM consumers: model weights and the KV cache. We’ll examine how quantization shrinks model weights and how KV cache techniques reclaim memory for higher batch sizes.
Running large language models in production requires more than loading a checkpoint and calling forward(). The gap between research benchmarks and real-world serving is defined by latency targets, memory budgets, and cost constraints that research papers rarely address.
