AI inference engines make chatbots faster by managing GPU memory, processing requests together, and reducing delays during heavy user traffic..Continuous batching adds new requests whenever others finish, keeping GPUs busy and helping AI systems handle more users at once..PagedAttention divides stored conversation data into smaller memory blocks, reducing memory waste and allowing more chats to run together..KV caching saves information from previously processed tokens, helping LLMs avoid repeating the same calculations while generating responses..Prefix caching reuses common instructions and prompts, allowing AI models to skip repeated processing and respond faster to similar requests..Quantization compresses model weights into smaller formats like INT8, INT4, and FP8, reducing GPU memory use and speeding up inference..Speculative decoding uses a smaller AI model to predict upcoming tokens, while a larger model checks them to accelerate text generation..Smart scheduling distributes requests across GPUs, while chunked prefill prevents long prompts from slowing down other active conversations..Inference engines such as vLLM, SGLang, and TensorRT-LLM combine these techniques to improve response speed, GPU efficiency, and user capacity..Read More Stories!.Join our WhatsApp Channel to get the latest news, exclusives and videos on WhatsApp