
Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs
Olmo-core 3 is designed to scale MoE training into the trillion-parameter range while preserving computational efficiency. It’s one of the core systems behind the next generation of Olmo, and part of our ongoing commitment to open up the tools and training infrastructure behind each new model. Training large AI models takes a lot of compute, driving up costs and energy use and putting advanced model development out of reach for many academic researchers and smaller labs.
- ▪Olmo-core 3 is designed to scale MoE training into the trillion-parameter range while preserving computational efficiency.
- ▪It’s one of the core systems behind the next generation of Olmo, and part of our ongoing commitment to open up the tools and training infrastructure behind each new model.
- ▪Training large AI models takes a lot of compute, driving up costs and energy use and putting advanced model development out of reach for many academic researchers and smaller labs.
Hugging Face Blog files mainly under ai. We currently carry 39 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 | Hugging Face Blog |
| Canonical URL | https://huggingface.co/blog/allenai/olmocore3 |
| Publication time | Thu, 01 Oct 2026 15:01:43 GMT |
| Retrieval time | 2026-10-01T15:02:31.828Z |
| Last seen | 2026-10-01T15:02:31.828Z |
| 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 | fWqHhplL9BCP · 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
Back to Articles Introducing Olmo-core 3: Open, scalable training infrastructure for large MoEs Enterprise Article Published October 1, 2026 Upvote - Kyle Wiggers Ai2Comms Follow allenai Building a training stack around how MoEs actually work Scaling and optimizing MoE training Scaling into the trillion-parameter range Built for the next generation of Olmo, open for everyone 📄 Tech Report | 💻 Code | 🧩 Interactive demo Today we’re releasing Olmo-core 3, a significant upgrade to our framework for developing large language models featuring a redesigned open mixture-of-experts (MoE) training system. Olmo-core 3 is designed to scale MoE training into the trillion-parameter range while preserving computational efficiency.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Hugging Face Blog.