PyTorch 2.12 Release
The release of PyTorch 2.12 introduces significant performance improvements and new features. Key enhancements include a batched linalg.eigh on CUDA that is up to 100x faster and a new device-agnostic Graph API for unified graph capture and replay. This version continues to evolve PyTorch into a versatile platform for production training and inference across various hardware.
- ▪PyTorch 2.12 features a batched linalg.eigh on CUDA that is up to 100x faster due to an updated cuSolver backend.
- ▪The new torch.accelerator.Graph API unifies graph capture and replay across multiple backends.
- ▪Adagrad now supports a fused variant, reducing kernel launch overhead and improving performance.
Hacker News (Newest) files mainly under programming. We currently carry 5,306 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 | Pytorch |
| Canonical URL | https://pytorch.org/blog/pytorch-2-12-release-blog/ |
| Publication time | Thu, 21 May 2026 02:22:31 +0000 |
| Retrieval time | 2026-05-21T02:35:03.339Z |
| Last seen | 2026-05-21T02:35:03.339Z |
| 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 | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| 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
Featured projects We are excited to announce the release of PyTorch® 2.12 (release notes)! The PyTorch 2.12 release features the following changes: Batched linalg.eigh on CUDA is up to 100x faster due to updated cuSolver backend selection New torch.accelerator.Graph API unifies graph capture and replay across CUDA, XPU, and out-of-tree backends torch.export.save now supports Microscaling (MX) quantization formats, enabling full export of aggressively compressed models Adagrad now supports fused=True, joining Adam, AdamW, and SGD with a single-kernel optimizer implementation torch.cond control flow can now be captured and replayed inside CUDA Graphs ROCm users gain expandable memory segments, rocSHMEM symmetric memory collectives, and FlexAttention pipelining This release is composed of…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Pytorch.