How AI Models Scale Beyond a Single GPU Across LLM Workloads

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Technical explainer on how large AI models run across multiple GPUs using distributed inference. Covers four parallelism strategies (data, pipeline, tensor, expert), KV cache optimization, and separating prefill/decode workloads. Designed for ML engineers and AI infrastructure developers building production LLM systems.