Inference latency in LLMs is reduced through optimization techniques like quantization, pruning, and efficient serving architectures. Quantization reduces numerical precision, such as converting 32-bit computations to 16-bit or 8-bit, which decreases processing time and memory usage. Pruning removes less important parameters, reducing the computational load without significantly affecting accuracy.
Hardware acceleration plays a crucial role in minimizing latency. GPUs, TPUs, and custom AI accelerators optimize matrix operations, which are the core computations in transformers. Additionally, frameworks like NVIDIA Triton and TensorRT provide inference optimization, enabling faster and more efficient model deployment.
Parallel processing and batch inference also reduce latency by processing multiple requests or tokens simultaneously. In real-time applications, techniques like caching intermediate computations and limiting output length further enhance response times. These strategies ensure LLMs deliver high performance in latency-sensitive environments, such as chatbots or search engines.