Flash Linear Attention: Efficient Attention Mechanisms for Transformers

Fri, 01 May 2026 00:00:00 +0000

The transformer architecture has been the dominant model for sequence processing since its introduction, but it carries a fundamental limitation: the self-attention mechanism scales with O(n^2) complexity relative to sequence length. For the long contexts increasingly demanded by modern AI applications – 128K tokens, 1M tokens, and beyond – this quadratic bottleneck becomes prohibitive. Flash Linear Attention provides a practical escape from this limitation.

The fla-org/flash-linear-attention repository brings together state-of-the-art research on linear attention mechanisms into a cohesive, optimized library. It provides CUDA-accelerated implementations of multiple linear attention variants that reduce complexity from O(n^2) to O(n), enabling transformer models to process sequences orders of magnitude longer than would be possible with standard attention.

llm.c: Karpathy's Minimal C Implementation of LLM Training

Fri, 01 May 2026 00:00:00 +0000

Most developers and researchers who work with large language models interact with them through high-level frameworks like PyTorch or Hugging Face Transformers. These frameworks hide immense complexity behind elegant APIs, but they also obscure the fundamental mechanics of how these models actually learn. llm.c tears away that abstraction, providing a complete, working implementation of GPT-2 training in pure C.

Created by Andrej Karpathy (formerly Director of AI at Tesla, co-founder of OpenAI), llm.c is first and foremost an educational project. It implements the entire forward pass, backward pass, and training loop for a transformer language model using nothing but standard C libraries, without a single dependency on PyTorch, TensorFlow, or any machine learning framework.

Transformer on SoloSoft

Flash Linear Attention: Efficient Attention Mechanisms for Transformers

llm.c: Karpathy's Minimal C Implementation of LLM Training