Rai: CPU-only LLM inference engine in pure Rust
RAI A CPU-only LLM inference engine written in Rust. RAI runs 4-bit quantized language models with hand-written AVX2 kernels — no GPU, no CUDA, no Python runtime, no PyTorch, no GGML, no BLAS. Load a .raimodel file and generate text on any supported x86-64 machine.
- ▪RAI A CPU-only LLM inference engine written in Rust.
- ▪RAI runs 4-bit quantized language models with hand-written AVX2 kernels — no GPU, no CUDA, no Python runtime, no PyTorch, no GGML, no BLAS.
- ▪Load a .raimodel file and generate text on any supported x86-64 machine.
Hacker News (AI / LLM) files mainly under ai. We currently carry 7,355 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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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/Classevelabs/rai |
| Publication time | Fri, 02 Oct 2026 17:23:12 +0000 |
| Retrieval time | 2026-10-02T17:46:13.739Z |
| Last seen | 2026-10-02T17:46:13.739Z |
| 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 | Gt_tc0cXCFEW · 1 stories |
| 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
RAI A CPU-only LLM inference engine written in Rust. RAI runs 4-bit quantized language models with hand-written AVX2 kernels — no GPU, no CUDA, no Python runtime, no PyTorch, no GGML, no BLAS. Load a .raimodel file and generate text on any supported x86-64 machine. Built by ClassEve. Licensed under Apache-2.0. Official repository. This is the only official repository for RAI. ClassEve's complete list of official accounts is at classeve.com/official. The GitHub account github.com/ClassEve is an unrelated third party, not affiliated with ClassEve. Measured performance Measured on a consumer-grade laptop CPU (4 cores / 8 threads), 2026-08-09, RAI 0.2.0. Full method, roofline, and the results that came out negative are in BENCHMARKS.md.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.