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Scaling Model Training with Distributed Computing
Distributed training splits the work across machines, but keeping them in step costs time. In real PyTorch data-parallel runs with one, two and four workers, every replica stayed identical, yet small batches trained slower with more workers and only large batches gained, about one and a half times with four.
vLLM and Model Serving: Scaling LLM Inference
Running a model for one user is easy. Serving it to hundreds at once is a scheduling and memory problem. This guide explains decode steps, batching and the key-value cache, simulates why continuous batching keeps a GPU busy, and shows how vLLM is started and called through an OpenAI-compatible API.