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What Makes Vera Rubin Different

Compute density

Each Rubin GPU delivers 50 PFLOPS of NVFP4 inference and 35 PFLOPS of training performance - 5x and 3.5x more than Blackwell B200 respectively - while transistor count grew only 1.6x (to 336 billion). That efficiency gain is what translates into more intelligence per watt, per rack, and per rupee.

HBM4 memory

Vera Rubin is the first platform on HBM4, nearly tripling memory bandwidth to 22 TB/s per GPU (versus 8 TB/s on Blackwell's HBM3e) with 288GB of capacity per GPU. In practice, that removes the concurrency bottlenecks that show up in long-context inference and high-batch Mixture-of-Experts serving.

NVLink 6 rack fabric

NVLink 6 doubles GPU-to-GPU bandwidth to 3.6 TB/s per GPU. Scaled across a full DGX Rubin NVL72 rack, that becomes a 260 TB/s all-to-all fabric - enough for all 72 GPUs to behave as one accelerator when serving trillion-parameter models.

Groq 3 LPX for disaggregated inference

Pairing Vera Rubin NVL72 racks with NVIDIA's Groq 3 LPX racks splits inference into two specialized stages: Rubin GPUs handle the compute-heavy prefill phase, while Groq LPUs handle token generation. The combination pushes inference throughput per megawatt to roughly 35x Blackwell for trillion-parameter, decode-heavy workloads.

Cableless, modular hardware

The 3rd-generation MGX NVL72 rack replaces cabling in the compute tray with board-to-board connectors, cutting tray installation time from around two hours (Blackwell) to five minutes. Over 80 MGX ecosystem partners support deployment and lifecycle management at this design.

Faster networking, offloaded I/O

ConnectX-9 SuperNICs support up to 1.6 Tb/s per port across Quantum-X800 InfiniBand (training) and Spectrum-X Ethernet (inference), while BlueField-4 DPUs offload storage and security processing so GPU and CPU cycles stay dedicated to AI work.

Cyfuture AI's liquid-cooled facility

Every Vera Rubin deployment on Cyfuture AI runs inside India's first 10MW Direct Liquid Cooled AI data center, built to support 240 kW/rack density with a PUE under 1.3 - comfortably covering Vera Rubin's 227 kW/rack power draw. It's currently the only facility in India purpose-built for this platform

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Technical Specifications
NVIDIA Rubin GPU

The compute core of the platform. Figures below are as confirmed by NVIDIA at CES and GTC 2026.

Component Specification
Architecture NVIDIA Rubin (3rd-gen MGX, TSMC N3 3nm process)
Transistors 336 billion (1.6x vs Blackwell B200's 208 billion)
Package design Dual reticle-sized compute dies + I/O dies (CoWoS-L packaging)
GPU memory 288 GB HBM4 per GPU
Memory bandwidth Up to 22 TB/s per GPU (vs 8 TB/s HBM3e on Blackwell)
NVFP4 inference 50 PFLOPS per GPU (5x vs Blackwell B200)
NVFP4 training 35 PFLOPS per GPU (3.5x vs Blackwell B200)
NVLink generation NVLink 6 — 3.6 TB/s bidirectional per GPU
PCIe interface PCIe Gen 6
Cooling 100% liquid cooled (fanless, tubeless, cableless chassis)

NVIDIA DGX Rubin NVL72 Rack-Scale Configuration

The flagship configuration: 72 Rubin GPUs and 36 Vera CPUs unified through NVLink 6 in a single rack.

Component Specification
System name NVIDIA DGX Rubin NVL72 (3rd-gen MGX design)
GPUs per rack 72 × Rubin GPU (288GB HBM4 each)
CPUs per rack 36 × NVIDIA Vera CPU (88-core Olympus ARM, Armv9.2, SMT-176 threads)
Total HBM4 GPU memory 20.7 TB per rack
Total CPU memory 54 TB LPDDR5x per rack (up to 1.5 TB SOCAMM per Vera CPU)
Total HBM4 bandwidth 1.6 PB/s per rack
NVLink fabric (scale-up) 260 TB/s all-to-all within the rack
Rack inference performance 3.6 EFLOPS NVFP4 standard; up to 8 EFLOPS with CPX variant
Rack training performance 2.5 EFLOPS NVFP4
CPU-GPU coherent bandwidth 1.8 TB/s per pair (NVLink-C2C)
Scale-out networking Quantum-X800 InfiniBand and Spectrum-X Ethernet
Networking NICs ConnectX-9 SuperNICs, up to 1.6 Tb/s per port
DPU BlueField-4 (storage and security offload)
Power per rack ~227 kW (requires DLC-capable infrastructure)
Cooling Direct Liquid Cooling (DLC-2); in-rack/in-row CDU, cold plates, no fans
Chassis design Cable-free modular tray; board-to-board connectors; ~5-minute tray installs
Rack form factor 3rd-gen NVIDIA MGX NVL72 (upgrade-compatible with prior gen)
Deployment availability H2 2026, via major cloud and OEM partners

NVIDIA HGX Rubin NVL8
Enterprise Server Configuration

A server-form-factor entry point: 8 Rubin GPUs with an Intel Xeon 6 host, built for standard data center racks.

Component Specification
System name NVIDIA HGX Rubin NVL8
GPU count 8 × Rubin GPU
Host CPU Intel Xeon 6 (multi-socket)
Total GPU memory 2.3 TB HBM4 (8 × 288 GB)
Total GPU memory bandwidth 176 TB/s (8 × 22 TB/s)
Interconnect NVLink 6 within the NVL8 baseboard
Network ConnectX-9 SuperNICs for scale-out
Cooling Direct Liquid Cooling
Use case Enterprise AI training, inference, and HPC in standard racks
OEM system builders Cisco, Dell, HPE, Lenovo, Supermicro

NVIDIA Vera CPU

The compute core of the platform. Figures below are as confirmed by NVIDIA at CES and GTC 2026.

Component Specification
CPU architecture Custom NVIDIA Olympus ARM cores (Armv9.2)
Core count 88 cores, Spatial Multi-Threading (176 effective threads)
Transistors 227 billion
Memory type LPDDR5x (SOCAMM modules)
Memory per CPU Up to 1.5 TB
CPU memory bandwidth Up to 1.2 TB/s
CPU-GPU coherent link NVLink-C2C at 1.8 TB/s, unified memory space
vs. Grace CPU 2x improvement in data processing, compression, and CI/CD workloads

Generation-on-Generation:
Hopper → Blackwell → Vera Rubin

A direct look at how the Rubin GPU compares to NVIDIA's previous flagship.

Component Specification
Generation-on-Generation Hopper → Blackwell → Vera Rubin
Comparison A direct look at how the Rubin GPU compares to NVIDIA's previous flagship.
GPU memory 141 GB HBM3e → 288 GB HBM4
Memory bandwidth 4.8 TB/s → 22 TB/s (2.75x vs Blackwell)
NVFP4 inference ~10 PFLOPS (FP8 equiv.) → 50 PFLOPS (5x vs Blackwell B200)
NVFP4 training ~8 PFLOPS (FP8 equiv.) → 35 PFLOPS (3.5x vs Blackwell B200)
NVLink bandwidth 900 GB/s (NVLink 4) → 3.6 TB/s (NVLink 6)
Transistors 80 billion → 336 billion
Process node TSMC N4 (4nm) → TSMC N3 (3nm)
Rack NVLink fabric 900 GB/s per GPU → 260 TB/s all-to-all (NVL72)
Inference token cost Baseline → 1/10th the cost (vs GB200 NVL72)
MoE training GPU efficiency Baseline → 4x fewer GPUs required
Cooling Air / liquid hybrid → 100% direct liquid cooled

Where It Fits: Real-World Applications

Agentic AI at enterprise scale

Agentic AI at enterprise scale

Agentic workloads can consume up to 15x more tokens than a typical chat application, which is exactly where Vera Rubin's cost-per-token advantage matters most. With 20.7 TB of unified HBM4 memory across a full NVL72 rack, trillion-parameter models can fit entirely within one rack's memory space — removing the cross-rack overhead that sub-100B deployments usually avoid but larger ones don't.

Trillion-parameter LLM training

Trillion-parameter LLM training

For frontier labs training large Mixture-of-Experts models, Vera Rubin completes comparable MoE training runs with a quarter of the GPU count needed on GB200 NVL72 — a direct cut to cluster footprint and infrastructure cost.

Long-context inference

Long-context inference

HGX Rubin NVL8 and NVL72 configurations (with an optional NVL144 CPX variant for prefill-heavy workloads) support production deployment of million-token context windows — useful for legal document review, genomic analysis, enterprise knowledge search, and generative video, where context length is the real bottleneck.

High-performance scientific computing

High-performance scientific computing

The same FP64 double-precision compute and memory bandwidth that helps with AI also suits simulation-heavy work: molecular dynamics, climate modeling, computational fluid dynamics, quantum chemistry, and drug discovery pipelines.

Real-time generative AI in production

Real-time generative AI in production

For consumer-facing generative AI serving millions of concurrent users, the throughput-per-watt gains translate directly into lower cost per query and lower latency at scale.

AI factories

AI factories

A single Vera Rubin NVL72 Scalable Unit — 1,152 GPUs across 16 racks — runs within a 5MW power envelope, with 331 TB of HBM4 memory and 1.6 PB/s of aggregate bandwidth, all coherently addressable across the NVLink fabric. Cyfuture AI's 10MW facility is designed to host multiple Scalable Units at that density.

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Limits on NVIDIA Vera Rubin

Unlock breakthrough performance for large language models, AI factories,
and next-generation AI workloads with NVIDIA Vera Rubin and enterprise-grade infrastructure from Cyfuture AI.

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H200 GPUs
Why Access Vera Rubin through Cyfuture AI

Why Access Vera Rubin through Cyfuture AI

Cyfuture AI is India's infrastructure partner for this generation of hardware. A few things that set the offering apart:

Liquid-cooled infrastructure: India's first 10MW Direct Liquid Cooled AI data center, and currently the only facility in the country built for Vera Rubin's 227 kW/rack thermal load.
GPU-as-a-Service, no CapEx: Access DGX Rubin NVL72 and HGX Rubin NVL8 configurations on usage-based pricing, with no upfront hardware commitment.
Turnkey deployment: Cyfuture AI handles the full stack - power and cooling design, cluster configuration, NVLink fabric validation, and software provisioning.
Full NVIDIA software stack: CUDA 12+, NIM microservices, Triton, TensorRT-LLM, PyTorch, JAX, TensorFlow, RAPIDS, and NVIDIA AI Enterprise — pre-validated on Vera Rubin.
Technical support: Infrastructure architects with GPU cluster, distributed training, and inference optimization experience, available 24x7.
Scales with you: Multi-rack expansion via Quantum-X800 InfiniBand, from a single NVL72 rack up to multi-rack DGX SuperPOD clusters.
NVIDIA Cloud Partner status: Enterprise-grade certifications backing every deployment for reliability, security, and compliance.
Cyfuture AI is currently the only data center in India purpose-built for NVIDIA Vera Rubin GPU Server deployments.

Voices of Innovation: How We're Shaping AI Together

We're not just delivering AI infrastructure-we're your trusted AI solutions provider, empowering enterprises to lead the AI revolution and build the future with breakthrough generative AI models.

KPMG optimized workflows, automating tasks and boosting efficiency across teams.

H&R Block unlocked organizational knowledge, empowering faster, more accurate client responses.

TomTom AI has introduced an AI assistant for in-car digital cockpits while simplifying its mapmaking with AI.

Performance at a Glance

Specification GB300 NVL72 (Blackwell Ultra)
50 PFLOPS NVFP4 inference per Rubin GPU (5x vs Blackwell B200)
288 GB HBM4 GPU memory per Rubin GPU — 1.5x more than Blackwell B200
22 TB/s Memory bandwidth per GPU — 2.75x vs HBM3e Blackwell
260 TB/s All-to-all NVLink 6 fabric bandwidth across one NVL72 rack
1/10th Inference token cost vs NVIDIA GB200 NVL72
1/4th GPUs needed for MoE training vs GB200 NVL72
72 GPUs + 36 CPUs Unified in one rack via NVLink 6 — operates as a single accelerator
227 kW/rack Power envelope, hosted in Cyfuture AI's 240 kW/rack liquid-cooled DC
5 minutes Compute tray installation time (vs 2 hours on Blackwell)
3.6 TB/s NVLink 6 bidirectional GPU-to-GPU bandwidth per GPU

The Platform: Seven Co-Designed Chips, One System

Vera Rubin isn't just a GPU — it's seven chips engineered to work as a single AI factory architecture. All of it is available through Cyfuture AI's Vera Rubin infrastructure:

Chip Role
Rubin GPU 336B transistors, HBM4, 50 PFLOPS NVFP4, NVLink 6 — primary compute engine
Vera CPU 88-core Olympus ARM, 1.5TB LPDDR5x, 1.2 TB/s bandwidth — feeds the GPU, manages KV cache and prefill
NVLink 6 switch 3.6 TB/s per GPU, 260 TB/s all-to-all in NVL72 — unified memory fabric across the rack
ConnectX-9 SuperNICs Up to 1.6 Tb/s — scale-out networking across racks and pods
BlueField-4 DPUs Storage and security offload — frees GPU/CPU compute for AI workloads
Spectrum-6 Ethernet Photonic-grade Ethernet for scale-out AI inference clusters
Groq 3 LPX (optional) Decode-phase inference accelerator — paired with Vera Rubin for ~35x throughput/MW on trillion-parameter models

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FAQs: NVIDIA Vera Rubin

The power of AI, backed by human support

At Cyfuture AI, we combine advanced technology with genuine care. Our expert team is always ready to guide you through setup, resolve your queries, and ensure your experience with Cyfuture AI remains seamless. Reach out through our live chat or drop us an email at [email protected] - help is only a click away.

Three areas account for most of the gap: memory (HBM4 at 22 TB/s vs HBM3e at 8 TB/s), compute (50 PFLOPS NVFP4 vs roughly 10 PFLOPS on H200), and interconnect (NVLink 6 at 3.6 TB/s vs NVLink 4/5). Combined, that's around 5x inference performance and a tenth of the inference token cost versus Blackwell NVL72.

NVL72 is the rack-scale flagship — 72 GPUs and 36 CPUs unified in one rack, built for AI-factory-scale training and frontier model inference. NVL8 is an 8-GPU server with an Intel Xeon 6 host, meant for enterprises that want Rubin-class compute in a standard rack without deploying a full NVL72 system.

Trillion-parameter MoE training, agentic AI inference, million-token context windows, high-throughput generative AI, and scientific computing all see the biggest gains. For inference under roughly 70B parameters, Blackwell still holds up well — Vera Rubin's economics really kick in above 200B parameters.

Yes. The 10MW liquid-cooled facility scales from a single NVL72 rack up through multi-rack DGX SuperPOD configurations, using Quantum-X800 InfiniBand for training clusters and Spectrum-X Ethernet for inference. Deployment — power, cooling, NVLink validation, software — is handled end to end.

PyTorch, TensorFlow, JAX, CUDA 12+, TensorRT-LLM, Triton, RAPIDS, vLLM, NVIDIA AI Enterprise, and NIM microservices — all pre-validated before delivery.

NVL72 racks run at roughly 227 kW each, which needs Direct Liquid Cooling. Cyfuture AI supports up to 240 kW/rack with DLC-2 cooling, in-rack/in-row CDU, RDHx sidecar support, and a PUE under 1.3 — built specifically to handle this without power or thermal compromises.

NVIDIA has Vera Rubin in production as of Q1 2026, with partner availability landing in H2 2026. Cyfuture AI offers it as GPU-as-a-Service, with usage-based pricing for HGX Rubin NVL8 and DGX Rubin NVL72, plus enterprise and reserved-capacity terms for sustained workloads. Contact the team for early access and a quote based on your workload.

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