Inference Optimization
Techniques for making AI model predictions faster and cheaper in production, including quantization, batching, caching, and specialized serving infrastructure.
Key Techniques
Quantization: Reduce numerical precision (FP16, INT8, INT4). KV cache: Avoid recomputing previous tokens. Continuous batching: Dynamically group requests. Speculative decoding: Use a small model to draft, large model to verify.
Serving Frameworks
vLLM (PagedAttention), TensorRT-LLM (NVIDIA), TGI (Hugging Face), and Triton Inference Server. These handle the complexities of efficient GPU utilization and request scheduling.
Cost Impact
Optimization can reduce inference costs by 5-10x. For production LLM applications serving millions of requests, this translates to millions of dollars saved annually.