Step-by-step tutorial for implementing GQA, RoPE, and an online-softmax Flash-Attention forward pass in pure PyTorch — a portfolio project that signals real systems-level ML skill.
A complete, runnable walkthrough of implementing flash attention from scratch in NumPy, including online softmax, block tiling, causal masking, and correctness checks — designed as a CV-worthy systems project.
Build flash attention from scratch in pure NumPy using tiling and the online softmax trick, then extend it toward production. A deep, runnable project that signals real systems engineering to hiring managers.
A working engineer’s guide to implementing Flash Attention from the ground up using tiling and the online softmax trick — runnable NumPy, end-to-end tests, and a roadmap to senior-level extensions.
Table of Contents Introduction Why Inference Performance Matters at Scale Fundamentals of Quantization 3.1 Static vs. Dynamic Quantization 3.2 Post‑Training Quantization (PTQ) Techniques 3.3 Quantization‑Aware Training (QAT) Flash Attention: Reducing Memory Footprint of Self‑Attention 4.1 Algorithmic Overview 4.2 GPU‑Specific Optimizations Putting It All Together: A Practical Pipeline 5.1 Environment Setup 5.2 Quantizing a Hugging Face Model with BitsAndBytes 5.3 Enabling Flash Attention in Transformers 5.4 Benchmarking End‑to‑End Latency and Throughput Scaling Strategies Beyond Quantization & Flash Attention 6.1 Batching & Prefill/Decode Separation 6.2 Tensor Parallelism & Pipeline Parallelism 6.3 Model Sharding on Multi‑GPU Nodes Real‑World Case Studies 7.1 Chatbot Deployment for a Fortune‑500 Customer Service 7.2 Document Retrieval Augmented Generation (RAG) at Scale Best Practices & Common Pitfalls Conclusion Resources Introduction Large language models (LLMs) have moved from research curiosities to production‑grade components powering chatbots, code assistants, and retrieval‑augmented generation pipelines. As model sizes climb into the hundreds of billions of parameters, inference performance becomes a decisive factor for cost, user experience, and environmental impact. Two techniques have risen to the forefront of performance engineering for LLM inference: ...