Show HN: Tiny-vLLM – high performance LLM inference engine in C++ and CUDA
Tiny-vLLM is a high-performance LLM inference engine built using C++ and CUDA. It serves as both a learning tool and a teaching resource, providing full source code and a course on implementing the engine. The project aims to maximize hardware efficiency for fast responses and simultaneous prompt handling.
- ▪Tiny-vLLM is a smaller sibling of vLLM designed for high-performance LLM inference.
- ▪The repository includes source code and a course for learning and teaching purposes.
- ▪The engine supports various features like KV cache, static batching, and online softmax.
Opening excerpt (first ~120 words) tap to expand
tiny-vllm You're going to build a high performance LLM inference engine with C++ and CUDA - tiny-vllm, a younger and smaller sibling of vLLM We will learn a lot along the way, make mistakes and derive the ideas and maths from scratch This repository consists of two things: 1. a full source code of the inference server and 2. a course where I lead you through the process of implementing the engine. Feel invited to use it as a learning tool on your learning path or if you are a lecturer, feel welcome to use it as a teaching resource at your university The inference engine consists of: load a real LLM model from Safetensors (Llama 3.2 1B Instruct) full LLM forward pass (prefill + decode) all computation with CUDA kernels KV cache static batching continuous batching online softmax,…
Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.