Inference Optimization: Making AI Faster and Cheaper

Training a model is expensive, but inference—the process of running the model for users—is where the long-term costs accumulate. To make LLMs viable for global scale, engineers use several advanced optimization techniques.

1. KV Caching

In a transformer, every time you generate a new token, the model needs to “look back” at all previous tokens. Key-Value (KV) Caching stores the past computations for those tokens, so the model only has to calculate the new token, drastically increasing speed.

2. Continuous Batching

Traditional batching waits for a set number of requests to arrive before processing them together. Continuous batching allows new requests to join the queue immediately as soon as a previous request finishes a single token, reducing idleness and improving throughput.

3. PagedAttention

Popularized by vLLM, PagedAttention treats memory like an operating system does. Instead of allocating the entire context window in advance (which wastes space), it breaks memory into small pages that can be allocated dynamically as the model generates text.

4. Quantization (AWQ & GPTQ)

By converting model weights from 16-bit floats to 4-bit integers, we can reduce the memory footprint by 75%. Modern techniques like AWQ (Activation-aware Weight Quantization) do this with almost zero loss in intelligence.

Why Optimization Matters

Faster inference doesn’t just mean a better user experience; it enables more complex “agentic” workflows that require hundreds of LLM calls to solve a single problem.