Inference Optimization
Techniques for making AI model inference faster, cheaper, and more efficient. This includes quantization, batching, caching, speculative decoding, and hardware optimization.
Why It Matters
Inference optimization directly impacts user experience and operating costs. A 2x speedup means half the hardware cost or twice the user capacity.
Example
Combining KV caching, continuous batching, INT8 quantization, and Flash Attention to serve an LLM at 3x the throughput and half the latency of a naive deployment.
Think of it like...
Like tuning a race car — the engine (model) stays the same, but optimizing every other component extracts dramatically better performance.
Related Terms
Inference
The process of using a trained model to make predictions on new, previously unseen data. Inference is what happens when an AI model is deployed and actively serving results to users.
Latency
The time delay between sending a request to an AI model and receiving the response. In ML systems, latency includes data preprocessing, model inference, and network transmission time.
Throughput
The number of requests or predictions a model can process in a given time period. High throughput means the system can serve many users simultaneously.
Quantization
The process of reducing the precision of a model's numerical weights (e.g., from 32-bit to 8-bit or 4-bit), making the model smaller and faster while accepting a small trade-off in accuracy.
Model Serving
The infrastructure and process of deploying trained ML models to production where they can receive requests and return predictions in real time. It includes scaling, load balancing, and version management.
KV Cache
Key-Value Cache — a mechanism that stores previously computed attention key and value vectors during autoregressive generation, avoiding redundant computation for tokens already processed.