Scalable AI

Table of Contents Introduction Background: Distributed Machine Learning Basics The Communication Bottleneck Problem Gradient Compression Techniques 4.1 Quantization 4.2 Sparsification 4.3 Selective Gradient Compression (SGC) Peer‑to‑Peer (P2P) Networking in Distributed Training 5.1 Parameter‑Server vs P2P 5.2 Overlay Networks and Gossip Protocols Merging SGC with P2P: Architectural Blueprint Practical Implementation Walk‑through 7.1 Environment Setup 7.2 Selective Gradient Compression Code 7.3 P2P Communication Layer Code 7.4 Training Loop Integration Real‑World Use Cases Performance Evaluation Best Practices and Common Pitfalls 11 Future Directions 12 Conclusion 13 Resources Introduction Training modern deep neural networks often requires hundreds or thousands of GPUs working together across data centers, edge clusters, or even heterogeneous devices. While the compute power of each node has grown dramatically, network bandwidth and latency have not kept pace. In large‑scale setups, the time spent moving gradients and model parameters between workers can dominate the overall training time, eroding the benefits of parallelism. ...

Introduction Large language models (LLMs) have demonstrated remarkable abilities in generating natural‑language text, answering questions, and performing reasoning tasks. Yet, their knowledge is static—the parameters learned during pre‑training encode information up to a certain cutoff date, and the model cannot “look up” facts that were added later or that lie outside its training distribution. Retrieval‑augmented generation (RAG) solves this limitation by coupling an LLM with an external knowledge source. The LLM formulates a query, a retrieval engine fetches the most relevant pieces of information, and the model generates a response conditioned on that context. At the heart of modern RAG pipelines lies the vector database, a specialized system that stores high‑dimensional embeddings and performs fast approximate nearest‑neighbor (ANN) search. ...