Inference Optimization Techniques

Inference Optimization Techniques

Running AI models efficiently requires more than just good hardware. There is a whole set of software optimization techniques that can dramatically improve inference speed and reduce memory usage. Understanding these techniques helps you get the most out of your hardware.

Batching is one of the most effective optimizations. Instead of processing one prompt at a time, you process multiple prompts together. The model weights are loaded once and shared across all prompts in the batch. This increases throughput dramatically, from maybe 50 tokens per second for a single user to thousands of tokens per second for a batch of 100 users.

Kernel fusion is a technique that combines multiple small operations into one larger operation. Instead of doing ten small matrix multiplications one after another, the fused kernel does them all at once. This reduces overhead from launching GPU kernels and improves memory usage because intermediate results stay on the GPU instead of being written to memory and read back.

Speculative decoding is a clever technique that uses a small, fast draft model to generate several candidate tokens, then has the large model verify them all at once. Since verifying a batch of tokens is almost as fast as generating one token, this can speed up generation by 2x to 3x. The small model guesses what the large model would say, and the large model checks the work.

Other important optimizations include Flash Attention, which reorders attention computations to use memory more efficiently, and continuous batching, where new requests are added to the batch as old ones finish instead of waiting for the entire batch to complete. Together, these optimizations can make a model run 5 to 10 times faster than a naive implementation.