B200 to Vera Rubin: What NVIDIA Changed Again

2026-04-11 · Source: Deep Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Emerging Technologies & Innovation · Depth: Expert, medium

Summary

NVIDIA announced its Vera Rubin architecture, slated for customer shipments in H2 2026, succeeding Blackwell. The Rubin GPU, built on TSMC's 3nm process, features 336 billion transistors, a 1.6x increase from Blackwell's 208 billion, delivering 50 PFLOPS of FP4 inference (2.8x B200). Memory is upgraded to HBM4, providing 288 GB at 22 TB/s bandwidth, a 1.5x capacity and 2.75x bandwidth improvement over B200's 192 GB HBM3e at 8 TB/s. The Vera CPU, with 88 Arm v9.2-A cores, doubles NVLink-C2C bandwidth to 1.8 TB/s and GPU-to-GPU NVLink 6 to 3.6 TB/s. The NVL72 rack-scale system, maintaining the same form factor, achieves 3.6 exaFLOPS of FP4 inference (5.6x GB200 NVL72) and reduces assembly time from 100 to 6 minutes. The platform expands to six chips, including the new Rubin CPX for long-context inference, BlueField-4 DPU, Spectrum-6 NIC, and Quantum-CX9 InfiniBand.

Key takeaway

For Directors of AI/ML evaluating future infrastructure, the Vera Rubin platform offers substantial performance gains, particularly for LLM inference due to its 2.75x memory bandwidth increase and the specialized Rubin CPX for long-context workloads. Your team should plan for H2 2026 deployments to capitalize on the 5-6x inference throughput improvement over current GB200 NVL72 systems and the integrated datacenter stack approach.

Key insights

NVIDIA's Vera Rubin platform significantly boosts AI compute and memory bandwidth, introducing specialized hardware for long-context inference.

Principles

• Memory bandwidth is critical for LLM inference performance.
• Rack-scale integration compounds individual component improvements.
• Specialized hardware optimizes specific AI workloads.

Method

The Vera Rubin platform integrates Rubin GPUs (3nm, HBM4), Vera CPUs (88 Arm cores), and NVLink 6 interconnects, alongside a new Rubin CPX for long-context inference, to deliver a full datacenter AI stack.

In practice

• Utilize Rubin CPX for long-context LLM inference clusters.
• Leverage NVLink 6 for faster multi-GPU collective operations.
• Consider the NVL72 for high-density, liquid-cooled AI deployments.

Topics

• Rubin GPU Architecture
• HBM4 Memory Technology
• NVLink Interconnects
• Rubin CPX
• Datacenter AI Infrastructure

Best for: CTO, Director of AI/ML, MLOps Engineer, AI Scientist, Machine Learning Engineer, AI Architect

Related on AIssential

• CES 2026: Nvidia promises five times the AI performance and ten times cheaper inference with Vera Rubin — Nvidia's new Vera Rubin platform and Alpamayo software aim to dominate the AI and autonomous driving value chains.
• Inside NVIDIA Groq 3 LPX: The Low-Latency Inference Accelerator for the NVIDIA Vera Rubin Platform — Heterogeneous AI inference architectures combining GPUs and LPUs significantly boost both throughput and low-latency responsiveness.
• Nvidia: Star Attraction at CES 2026 — Nvidia's Vera Rubin platform integrates co-designed chips and software to build scalable AI factories.
• H100 to B200: What Actually Changed — NVIDIA's Blackwell architecture, featuring the B200 GPU and GB200 NVL72 system, represents a fundamental shift from the H100, moving beyond individual GPU performance to datacenter-scale AI infrastructure.
• Another Giant Leap: The Rubin CPX Specialized Accelerator & Rack — Specialized hardware for prefill and decode phases of AI inference significantly optimizes cost and performance.
• The Rubin Era: How NVIDIA’s New Platform Rewrites the Rules for MoE and Agentic AI — NVIDIA's Vera Rubin platform redefines AI hardware by co-designing six chips for unprecedented performance in MoE and agentic AI.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Deep Learning on Medium.