Reports
AI-generated structured vendor updates
NVIDIA Rubin GPU Detailed: 3nm Dual-Die, 336B Transistors, 288GB HBM4, NVLink 6 Doubles Bandwidth
NVIDIA unveiled the full Rubin GPU architecture at SIGGRAPH 2026: 3nm dual-die, 336B transistors, 288GB HBM4 with 22 TB/s bandwidth, and NVLink 6 at 3600 GB/s. The NVL72 rack integrates 72 GPUs with 36 Vera CPUs, requiring full liquid cooling due to >1000W TDP.
NVIDIA-OpenAI $100B Partnership: 10GW Vera Rubin AI Factories Reshape Ecosystem
NVIDIA and OpenAI announce a strategic partnership to deploy at least 10GW of NVIDIA systems using the Vera Rubin platform (Rubin GPU, Vera CPU, HBM4, NVLink 6). NVIDIA will invest up to $100B. First facilities go online in H2 2026, powering OpenAI's next-gen models, marking the era of multi-GW AI factories.
TEST 2026-07-23 DailyShift 24h信号测试
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NVIDIA公开Rubin GPU架构细节：3360亿晶体管，智能体AI性能较Blackwell提升10倍
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NVIDIA Reveals Vera Rubin GPU and Vera CPU: 3360B Transistors, 88-Core Olympus, 10x Agentic AI Efficiency
NVIDIA fully discloses Vera Rubin GPU and Vera CPU specifications. The GPU features 3360B transistors, HBM4 288GB, and 10x agentic AI efficiency over Blackwell. The CPU has 88 custom Olympus cores, delivering 2.2x faster agentic AI performance than Intel Sapphire Rapids. This solidifies NVIDIA's full-stack strategy against x86 incumbents.
NVIDIA Vera Rubin Platform and Dynamo 1.0 Disaggregate Inference, Shift Focus to Intelligence per Dollar
NVIDIA unveils Vera Rubin platform with a 7-chip stack (Vera CPU, Rubin GPU, NVLink 6, etc.) and Dynamo 1.0 inference disaggregation. A single NVL72 rack packs 72 GPUs/36 CPUs with 1.6 PB/s bandwidth, achieving up to 7x inference performance. The new 'intelligence per dollar' metric signals a shift from training to inference cost competition.
NVIDIA Extreme Co-Design: Vera Rubin Platform Targets Agentic Inference TCO Inflection
NVIDIA unveils an extreme co-design stack for agentic systems, featuring Vera Rubin NVL72, NVLink 6, ConnectX-9, BlueField-4, and Spectrum-X. By disaggregating inference, optimizing KV cache management, and deploying low-latency fabrics, it aims to break the throughput-interactivity tradeoff, making high-context token processing economically viable.