Towards Speed-of-Light Text Generation with Nemotron-Labs Diffusion Language Models
Nemotron-Labs has introduced a new type of language model called diffusion language models (DLM) that allows for faster text generation. Unlike traditional autoregressive models that generate one token at a time, DLMs can generate multiple tokens in parallel and revise them iteratively. This innovation aims to enhance performance for latency-sensitive applications and improve the overall efficiency of language processing tasks.
- ▪Nemotron-Labs Diffusion models can generate multiple tokens in parallel, improving efficiency.
- ▪The models support three generation modes: autoregressive, diffusion, and self-speculation.
- ▪NVIDIA is releasing various scales of the models under different licenses for research flexibility.
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| Original publisher | Hugging Face - Blog |
| Canonical URL | https://huggingface.co/blog/nvidia/nemotron-labs-diffusion |
| Publication time | Sat, 23 May 2026 00:02:03 GMT |
| Retrieval time | 2026-05-23T00:07:03.467Z |
| Last seen | 2026-05-23T00:21:57.416Z |
| 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 | dzg-_ASYTEPg · 3 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 |
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| 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 Towards Speed-of-Light Text Generation with Nemotron-Labs Diffusion Language Models Enterprise + Article Published May 23, 2026 Upvote - Mehran Maghoumi MMaghoumi Follow nvidia Yonggan Fu YongganFu Follow nvidia Pavlo Molchanov pmolchanov Follow nvidia Khadkevich mkhadkevich Follow nvidia Quick Links to the Models, Training Recipe and Technical Report Three Generation Modes in One Model Performance Highlights How we trained Nemotron-Labs Diffusion Deployment and inference through SGLang Get Started Today Large language models (LLMs) have become the default interface for code generation, math problem solving, summarization, document understanding, and many other developer workflows.
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