What Is LLM Inference? How AI Models Generate Responses

Learn more about LLM Inference here → https://ibm.biz/~CCvvB5TvB Every LLM response starts with much more than a prompt. Grace Ableidinger explains LLM inference, from tokenization and GPU batching to KV cache, prefill, and decode. Learn how AI models turn static weights into real-time responses. 00:00 – Understand What LLM Inference Actually Does 01:15 – See How LLM Inference Engines Serve AI Models 01:43 – Follow an LLM Request from API to Model 02:15 – Turn Prompts Into Tokens with Tokenization 02:59 – Route LLM Requests to GPU Hardware 03:30 – Increase LLM Throughput with Continuous Batching 04:23 – Understand LLM Prefill and KV Cache 05:24 – See How Decode Generates Tokens 06:18 – Measure LLM Latency with TTFT and ITL 06:59 – Connect the Full LLM Inference Pipeline AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/~LqLIqIARH AI was used in the creation of the transcript and metadata for this video. #llm #aimodels #aiinference #ai #aiengineering --------------------------------------------------------------------------------------------------------- Find us on YouTube: 🔵 IBM Technology: https://www.youtube.com/@IBMTechnology 🔵 IBM: https://www.youtube.com/@IBM 🔵 IBM Developer: https://youtube.com/@IBMDeveloperAdvocates