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The biggest AI models can't fit on one GPU. Grace Ableidinger explains how distributed AI inference scales LLMs across GPUs using data, pipeline, tensor, and expert parallelism. Learn how KV cache, throughput, and prefill/decode bottlenecks shape production AI serving.
00:00 – See Why AI Models Need Distributed Inference
01:50 – Scale LLM Traffic with Data Parallelism
02:45 – Split AI Models with Pipeline Parallelism
03:45 – Divide LLM Layers with Tensor Parallelism
04:32 – Scale Mixture-of-Experts Across GPUs
05:44 – Separate LLM Prefill and Decode Workloads
07:51 – Combine GPU Parallelism for Production AI
08:29 – Orchestrate Distributed LLM Inference
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#llm #aimodels #gpu #machinelearning
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