Building a Mini GPT with ZeRO Stage 2 from Scratch in PyTorch
Learn how to implement a distributed training loop from scratch, mastering ZeRO Stage 2 and gradient accumulation to optimize memory and compute for large language models.
Distributed-Training
Inside NCCL Topologies: Collective Algorithms for Multi-GPU Training
Understanding NCCL topologies is essential for optimizing multi-GPU training performance. This post breaks down collective algorithms, hardware-aware patterns, and practical tuning tips.
Building Mixed-Precision AdamW with ZeRO-1 from Scratch in Pure PyTorch
A runnable, from-scratch implementation of mixed-precision AdamW with dynamic loss scaling and ZeRO-1 sharded optimizer state in pure PyTorch — built to demonstrate real systems engineering, not just model design.
Optimizing Distributed Model Training on Bare‑Metal Clusters with RDMA and Low‑Latency Interconnects
Introduction Training state‑of‑the‑art deep‑learning models now routinely requires hundreds of GPUs working in concert. While public cloud providers offer convenient, on‑demand clusters, many research labs and enterprises still prefer bare‑metal clusters for three core reasons: Predictable performance – no noisy neighbors, no hypervisor overhead. Cost efficiency at scale – amortized CAPEX and lower per‑GPU price. Full control over hardware and software – ability to fine‑tune network stacks, install custom drivers, and leverage specialized interconnects. When you combine bare‑metal hardware with RDMA (Remote Direct Memory Access) and low‑latency interconnects such as InfiniBand or RoCE (RDMA over Converged Ethernet), you can dramatically reduce the communication overhead that traditionally limits distributed training speed. This article walks through the entire optimization stack—from networking fundamentals to concrete PyTorch code—so you can extract the maximum throughput from your cluster. ...
Architecting Low‑Latency Edge Networks for Decentralized Large Language Model Training and Inference
Introduction Large language models (LLMs) such as GPT‑4, LLaMA, and PaLM have demonstrated unprecedented capabilities in natural‑language understanding, generation, and reasoning. Their size—often measured in billions or even trillions of parameters—demands massive compute, storage, and network resources. Historically, training and inference for these models have been confined to centralized data centers equipped with high‑performance GPU clusters and ultra‑low‑latency interconnects (e.g., NVLink, InfiniBand). However, a growing class of applications—autonomous vehicles, real‑time translation on mobile devices, edge‑based recommendation engines, and privacy‑sensitive AI assistants—cannot tolerate the round‑trip latency of sending data to a distant cloud. They require low‑latency, high‑throughput edge networks that can host decentralized training and inference workloads. This shift presents a unique set of architectural challenges: ...