Inference Speed
Inference speed refers to the rate at which a machine learning model generates output tokens or predictions after training. It is a critical metric for local-ai deployment, real-time applications, and cost efficiency.
Key Optimization Techniques
- Quantization: Reducing the precision of model weights (e.g., FP16, INT8, ternary-quantization) to decrease memory bandwidth requirements and accelerate computation.
- Speculative Decoding: Using a smaller “draft” model to propose tokens, which are then verified by the larger “target” model, significantly reducing the number of forward passes required per token.
- Model Architecture: Designing models for parallelism or efficient [processing pipelines to maximize hardware utilization.
- Prompt Engineering: Optimizing input structure to reduce unnecessary token consumption and improve generation efficiency. Recent guidelines for Anthropic Claude Opus 5.5 Prompting Rules and Optimization Guide highlight that legacy prompting methods may be less efficient or more costly with newer model versions, necessitating updated strategies to maintain optimal inference performance.