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
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#llm #aimodels #aiinference #ai #aiengineering
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