Show HN: Tiny-vLLM – high performance LLM inference engine in C++ and CUDA

10.2 relevance

New high-performance LLM inference engine, directly relevant.

2026-05-30 AI/ML github.com

Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM - jmaczan/tiny-vllm

Summary

tiny-vLLM is an open-source inference engine and educational course in C++ and CUDA, designed as a smaller sibling of vLLM. It implements full LLM inference for Llama 3.2 1B Instruct, covering prefill, decode, PagedAttention, continuous batching, and FlashAttention-like online softmax, all from scratch. The repository serves as both a production-grade server and a teaching resource for understanding GPU-accelerated inference.

Key Takeaways

Study tiny-vLLM's source code and course to gain hands-on understanding of CUDA kernel engineering and efficient LLM serving techniques.

Why it matters

For a solutions architect focused on AI/ML and cloud infrastructure, this provides a deep-dive into the low-level implementation of LLM inference, critical for optimizing deployment on GPU instances and understanding performance bottlenecks.

Author

jmaczan