What It Is
Where training learns from examples, inference applies what was learned, such as classifying an image, scoring a transaction, or generating text. It happens each time a user or system sends an input to the model. Training happens occasionally, but inference runs constantly in production.
Key Points
- Optimization: quantization, pruning, and compilation with TensorRT or ONNX Runtime.
- LLM serving: engines such as vLLM manage memory and batching.
- Deployment: cloud, on premises, or at the edge.
- Trade-offs: speed, accuracy, and cost.
Why It Matters
Inference runs on every request, so its speed and reliability directly shape the product experience. Faster inference means lower costs and a better user experience, and the right deployment location depends on latency targets, privacy needs, and budget.
How ClearLeaff Applies It
We build inference pipelines with sub-100ms responses for LLM workloads and under 8ms on edge hardware, balancing speed, accuracy, and cost.