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.
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,126 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/jmaczan/tiny-vllm |
| Publication time | Fri, 29 May 2026 19:38:27 +0000 |
| Retrieval time | 2026-05-29T19:45:02.813Z |
| Last seen | 2026-05-29T19:45:02.813Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | x-u1MK_2OpOh |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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.