vLLM

AutoRound is an open-source quantization toolkit for LLMs and VLMs, focused on high-accuracy low-bit inference across CPU, XPU, CUDA, and multiple deployment backends.

Open-source C++ and CUDA LLM inference engine inspired by vLLM, with a teaching-focused course that walks through model serving, batching, KV cache, and attention kernels.

Google Developers Blog post about integrating DFlash, a diffusion-style speculative decoding framework, into the vLLM TPU ecosystem to improve LLM serving speed on TPU v5p.

A public slide deck about vLLM-compile, a project focused on bringing compiler optimizations to LLM inference and speeding up torch.compile for vLLM workflows.