実世界分布に基づいた日本人ペルソナのための複合AIアプローチ 日本の AI の未来に向けたオープンデータ 高品質で多様なトレーニングデータなしに、日本文化を真に理解するAIを構築することはこれまでほぼ不可能でした。これを変えるため、NVIDIAは、日本の人口統計、地理的分布、文化的特性に沿ったペルソナを含む初のオープン合成データセット、Nemotron-Personas-Japan を公開しました。CC BY 4.0 ライセンスのもと提供される本データセットは、機微な個人データに依存することなく日本社会を反映した AI システム構築のための、プライバシー保護と規制対応を両立した基盤を提供します。 NVIDIA のエンタープライズ向け合成データ生成システム、NeMo Data Designer を用いて作成されたNemotron-Personas-Japan は、すでに広く利用されている US Personas データセットの成功を機に日本版として開発されました。本リリースは、各国・地域におけるソブリン AI 開発を支援する合成ペルソナデータセットとデータ構築方法のグローバルコレクションの第一弾です。 本データセットは、Nemotron モデルをはじめとするオープンソースの 大規模言語モデル(LLM) とシームレスに連携するよう設計されており、企業向けチャットボットから各種ドメインの AI エージェントに至るまで、日本語 AI アプリケーション向けのファインチューンを容易に行えるようになっています。 データセットの内容 合計600万件（各レコードにつき6ペルソナ、100万レコード）の自然な日本語で記述されたペルソナ 1レコードあたり22項目：6つのペルソナ関連項目と、公式の人口統計・労働統計に基づいた16のコンテキスト項目 総トークン数約14億：そのうち約8億5000万がペルソナ関連トークン 約95万件の固有の名前：合成データ生成で前例のない多様性    

People are trying to vibe code games. And it kind of works, at first. However, as the project grows, things begin to fall apart. Why? And what can we do about it? I’ll talk about the problem, how I fixed it, and where to go from here.

TL;DR: Qwen3-8B is one of the most exciting recent releases—a model with native agentic capabilities, making it a natural fit for the AIPC. With OpenVINO.GenAI, we’ve been able to accelerate generation by ~1.3× using speculative decoding with a lightweight Qwen3-0.6B draft. By using speculative decoding and applying a simple pruning process to the draft, we pushed the speedup even further to ~1.4× We wrapped this up by showing how these improvements can be used to run a fast, local AI […]

TL;DR – We’re excited to introduce the beta version of the Retrieval Embedding Benchmark (RTEB), a new benchmark designed to reliably evaluate the retrieval accuracy of embedding models for real-world applications. Existing benchmarks struggle to measure true generalization, while RTEB addresses this with a hybrid strategy of open and private datasets. Its goal is simple: to create a fair, transparent, and application-focused standard for measuring how models perform on data they haven’t seen before. The performance of many AI applications, […]

Evaluating the quality of AI-generated code is notoriously difficult. While humans can easily spot whether a piece of code “looks right,” determining if it actually works correctly, handles edge cases properly, and produces the intended result requires running and testing it. This is why today, we’re thrilled to announce BigCodeArena — the first human-in-the-loop platform for evaluating code generation models    

A compound AI approach to Indian personas grounded in real-world distributions Open Data for India’s AI Future India represents one of the world’s largest AI opportunities — with over 700 million internet users, a multitude of languages, and a rapidly growing developer ecosystem. Yet, most open datasets reflect Western norms and English-only contexts, creating a data gap that limits AI adoption in India’s multilingual, multi-script environment. Today, we’re releasing Nemotron-Personas-India, the first    

With the growing capability of large language models (LLMs), a new class of models has emerged: Vision Language Models (VLMs). These models can analyze images and videos to describe scenes, create captions, and answer questions about visual content. While running AI models on your own device can be difficult as these models are often computationally demanding, it also offers significant benefits: including improved privacy since your data stays on your machine, and enhanced speed and reliability because you’re not dependent […]

Intel and Hugging Face collaborated to demonstrate the real-world value of upgrading to Google’s latest C4 Virtual Machine (VM) running on Intel® Xeon® 6 processors (codenamed Granite Rapids (GNR)). We specifically wanted to benchmark improvements in the text generation performance of OpenAI GPT OSS Large Language Model(LLM). The results are in, and they are impressive, demonstrating a 1.7x improvement in Total Cost of Ownership(TCO) over the previous-generation Google C3 VM instances. The Google Cloud C4 VM instance further resulted in: […]

Let’s get straight to the point: worldwide, an estimated 220 million people suffer from at least one food allergy, and in the United States alone, this accounts for roughly 10% of the population. This means that if you don’t have an allergy, you’ll likely know someone who does — and it’s not a pleasant situation to be in. This condition affects not only patients’ physical health but also takes a significant toll on their mental well-being and overall quality of […]