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Nvidia, Microsoft, Amazon in Talks to Invest Up to $60 Billion in OpenAI — The Information https://www.theinformation.com/articles/nvidia-microsoft-amazon-talks-invest-60-billion-openai
Source: Amazon could invest up to $50B in OpenAI in coming weeks https://www.cnbc.com/2026/01/29/amazon-openai-investment-jassy-altman.html
An orchestration framework for small models that coordinate powerful tools – ToolOrchestra from NVIDIA It’s like a conductor model for agentic systems. Instead of solving everything itself, a small Orchestrator model reasons step-by-step and decides which tool or expert model”” https://x.com/TheTuringPost/status/2015565947827110255
Exclusive: China gives nod to ByteDance, Alibaba and Tencent to buy Nvidia’s H200 chips – sources | Reuters https://www.reuters.com/world/china/china-gives-green-light-importing-first-batch-nvidias-h200-ai-chips-sources-say-2026-01-28/
Missed Dynamo Day 2026? Our session on large-scale LLM serving with vLLM from @simon_mo_ is now available on NVIDIA On-Demand. Covers disaggregated inference, Wide-EP for MoE, and rack-scale deployments on GB200 NVL72. Thanks @nvidia for hosting! Watch recording:”” https://x.com/vllm_project/status/2017075057550618751
Nemotron 3 Nano in NVFP4 just dropped from @NVIDIA! 4x throughput on B200 (vs FP8-H100) with accuracy preserved via Quantization-Aware Distillation. The checkpoint is already supported by vLLM https://t.co/xd6JETkS6o 🤝Thanks NVIDIA × vLLM community!”” https://x.com/vllm_project/status/2016562169140433322
We just launched an ultra-efficient NVFP4 precision version of Nemotron 3 Nano that delivers up to 4x higher throughput on Blackwell B200. Using our new Quantization Aware Distillation method, the NVFP4 version achieves up to 99.4% accuracy of BF16. Nemotron 3 Nano NVFP4:”” https://x.com/NVIDIAAIDev/status/2016556881712472570
NVIDIA and CoreWeave Strengthen Collaboration to Accelerate Buildout of AI Factories | NVIDIA Newsroom https://nvidianews.nvidia.com/news/nvidia-and-coreweave-strengthen-collaboration-to-accelerate-buildout-of-ai-factories
New Stanford and NVIDIA’s paper that really worth your attention They introduced Test-Time Training to Discover (TTT-Discover), which lets models keep learning at inference time, using RL to find breakthrough solutions. It’s a new way to effectively solve scientific problems.”” https://x.com/TheTuringPost/status/2015377899168424073
I don’t think people have realized how crazy the results are from this new TTT + RL paper from Stanford/Nvidia. Training an open source model, they – beat Deepmind AlphaEvolve, discovered new upper bound for Erdos’s minimum overlap problem – Developed new A100 GPU kernels 2x”” https://x.com/rronak_/status/2015649459552850113
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