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NVIDIA News

Latest reporting, research and product updates filed under NVIDIA.

42 picksNewest firstLatest article Oct 2, 2026, 9:00 PM

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NVIDIA：Newsroom（RSS）AI score 61/100

NVIDIA Announces a $150 Billion Share Repurchase Authorization Increase

September 28, 2026 News Summary: NVIDIA’s Board of Directors has authorized a $150 billion increase to the share repurchase program, raising the remaining total program to $235 billion. This marks the largest share repurchase authorization increase in history. NVIDIA today announced that its Board of Directors has authorized an additional $150 billion under the company’s existing share repurchase program, increasing the total remaining amount authorized to $235 billion. The company expects to ex
NVIDIA Technical Blog：Agentic AI / Generative AIAI score 82/100

NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring

To understand where agentic AI stands today, consider the last seismic shift in technology: the rise of the internet in the 90s. It was new and full of possibilities. You could build a website over a weekend and share it with the world, or chat with someone half way around the world in online chat rooms without long-distance telephone fees. It brought endless opportunity, but also a lot of risk. A website could run code on your machine, steal your sensitive information, or infect your computer w
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LMSYS：Blog（Chatbot Arena 团队）AI score 63/100

Blog Chasing the Batch-1 Floor: Ling-3.0-flash Speculative Decode on Blackwell Batch-1 decode keeps getting more important. Xiaomi MiMo, for example, announced MiMo-V2.5-Pro UltraSpeed in June, claiming 1,000 tok/s decode on a one-trillion-parameter MoE model. Batch 1 gives an ... RadixArk SGLang Team, Ant Ling Infra Team

蚂蚁 Ling Infra 团队与 RadixArk SGLang 团队将 Ling-3.0-flash 混合线性注意力 MoE 模型的单请求解码速度从 288 tok/s 提升至 606 tok/s，平均 TPOT 从 3.33 ms 降至 1.53 ms。
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LMSYS：Blog（Chatbot Arena 团队）AI score 74/100

Blog SGLang Adds Day-0 Support for NVIDIA Nemotron 3.5 Lightning SGLang is excited to announce Day-0 support for NVIDIA Nemotron 3.5 Lightning, a customizable open model built to power always-on agents across local systems, the edge, the datacenter, and the cloud. ... NVIDIA Nemotron Team and SGLang Team

SGLang 宣布对 NVIDIA Nemotron 3.5 Lightning 提供 Day-0 支持，该开源模型为 30B 总参数、3B 激活参数的混合专家架构，支持最长 1M token 上下文，可从 Hugging Face 下载 BF16 和 NVFP4 权重。模型支持 MTP、DFlash、DSpark 三种投机解码技术，并可通过 OpenAI 兼容 API 接入智能体工作流。
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MarkTechPost（RSS）AI score 70/100

NVIDIA Releases Nemotron-Labs-3-Puzzle-75B-A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput at Matched User Throughput

NVIDIA 发布 Nemotron-3-Super 的压缩变体 Nemotron-Labs-3-Puzzle-75B-A9B，总参数从 120.7B 降至 75.3B，活跃参数从 12.8B 降至 9.3B，保持 88 块混合布局（40 Mamba、40 MoE、8 注意力）。在 8×B200 节点上，8K/64K 场景匹配用户吞吐量≥100 tok/s 时，服务器吞吐量提升 2.03 倍。单 H100 上 1M-token 并发从 1 增至 8，权重占用从 70 GB 降至 44.5 GB。迭代式 Puzzle 方法平均得分比单步高 0.57。代价：Arena-Hard-V2 降 4.2 分、SWE-Bench 降 2.6 分。Hugging Face 提供 BF16、FP8、NVFP4 检查点。
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Nathan Lambert：Interconnects（RSS）AI score 60/100

Artifacts 22: Zyphra, Cohere, and Poolside are expanding the breadth of the ecosystem

开源模型生态正变得更多元，参与者从少数中国公司扩展到全球各类组织。纯模型制造商包括 DeepSeek、智谱、MiniMax、Poolside、Arcee、Zyphra 及主权 AI 玩家 Cohere、Sovereign、Mistral、Trillion Labs；科技巨头如阿里 Qwen、Google Gemma 和 NVIDIA 各有不同动机；产品公司如 JetBrains、Zed、Krea、Photoroom 则训练高度专业的小模型。NVIDIA 发布 Nemotron-3-Ultra-550B-A55B-BF16，采用 LatentMoE 架构并改用 OpenMDW 许可证。Cohere 以 Apache 2.0 开源其旗舰模型 Command A+（05-2026-bf16），这是一款 218B-A25B MoE 模型，具备多模态、多语言和智能体能力。
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Hugging Face：Blog（RSS）AI score 66/100

Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel

NVIDIA NeMo AutoModel 是基于 Transformers v5 的开源库，添加 Expert Parallelism、DeepEP 融合 all-to-all 调度和 TransformerEngine 内核。在 MoE 模型微调中，相比原生 v5，训练吞吐量提升 3.4–3.7 倍，GPU 内存减少 29–32%，仅需改动一行 import。在 16 节点 128 张 H100 上全微调 Nemotron 3 Ultra 550B A55B 时，v5 因内存不足无法运行，而 AutoModel 凭借 EP=64 专家并行使训练可行。单节点 30B MoE 模型（如 Qwen3-30B-A3B）同样获得可量化的性能优势。
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Hugging Face：Blog（RSS）AI score 63/100

Towards Speed-of-Light Text Generation with Nemotron-Labs Diffusion Language Models

NVIDIA 发布 Nemotron-Labs Diffusion 系列，含 3B、8B、14B 文本模型和 8B 视觉-语言模型（VLM），均采用商用友好的 NVIDIA Nemotron Open Model License 或 NVIDIA Source Code License。模型支持自回归、扩散（逐块并行生成后逐步精炼）和自推测（扩散草拟候选 token 再自回归验证）三种模式。8B 模型平均准确率比 Qwen3 8B 提升 1.2%，扩散模式每次前向传递的 token 数（TPF）达自回归的 2.6 倍，自推测达 6–6.4 倍。模型在 1.3T tokens 上预训练、45B tokens 上微调，代码与模型已发布于 HuggingFace 和 GitHub，推理将获 SGLang 支持。
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