We introduce Block Sparse Attention, a library of sparse attention kernels that supports various sparse patterns, including streaming attention with token granularity, streaming attention with block granularity, and block-sparse attention. By incorporating these patterns, Block Sparse Attention can significantly reduce the computational costs of LLMs, thereby enhancing their efficiency and scalability. We release the implementation of Block Sparse Attention, which is modified base on FlashAttention 2.4.2.

Explore the latest advancement in TinyChat and AWQ – the integration of Visual Language Models (VLM) on the edge! The exciting advancements in VLM allows LLMs to comprehend visual inputs, enabling seamless image understanding tasks like caption generation, question answering, and more. With the latest release, TinyChat now supports leading VLMs such as VILA, which can be easily quantized with AWQ, empowering users with seamless experience for image understanding tasks.

Running large language models (LLMs) on the edge is of great importance. In this blog, we introduce TinyChat, an efficient and lightweight system for LLM deployment on the edge. It runs Meta's latest LLaMA-2 model at 30 tokens / second on NVIDIA Jetson Orin and can easily support different models and hardware.