Distributed Inference with PyTorch from First Principles

Understand and implemente DP, TP, and PP in Less Than 200 Lines python code Photo by Nana Dua on Unsplash 0. Preface Models keep getting bigger. Even if INT4 quantization squeezes the weights onto a single GPU, inference still has to pay for KV cache and activations, both of which scale with batch size and sequence length. In practice, single-GPU inference quickly hits the VRAM wall. Multi-GPU distributed inference is no longer optional. The problem is that if you jump straight into Megatron-LM, DeepSpeed, vLLM, or SGLang source code, the engineering layers can bury the core ideas.