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When Vera Rubin ships, most data centers will start retrofitting. Ours will already be running. India's only 10 MW liquid cool data center engineered for Blackwell, Grace Blackwell — and purpose-built for Vera Rubin's mandatory 100% direct-to-chip cooling. No retrofit. No wait. No compromise.
Not retrofitted from legacy colocation. Every MEP system, cooling loop, and power path was engineered around the thermal and electrical profile of modern GPU accelerators.
| Total IT Load | 10 MW IT load — scalable in phased deployment blocks |
| Cooling Arch. | Direct-to-chip liquid loops (D2C) · Rear-door heat exchangers (RDHx) · Hybrid air/liquid zones · Configurable CDUs and manifolds |
| Rack Density | ~20 kW to 240 kW+ per rack — configurable post OEM-specific thermal validation |
| Chipset | NVIDIA (H100/H200/B200/B300/Vera Rubin NVL72) · AMD Instinct · Intel Gaudi · Cloud ASICs · Inference accelerators · Custom OEM racks |
| Tenant Formats | Private suite · Secure cage · Dedicated hall · GPU cluster pod · Managed GPU cloud |
| Network | 400G/800G fabric-ready · InfiniBand NDR/XDR or Ethernet · Non-blocking spine-leaf · RoCEv2 support |
| Storage | High-throughput NVMe · Parallel file system · Object storage · AI Dataset Lake · Backup & archival |
| Power | N+1 / 2N redundancy · UPS-backed critical power · Generator backup · A+B dual-corded feeds · Branch circuit metering |
| Monitoring | DCIM · BMS · EPMS · Rack telemetry · Coolant temp/pressure/flow · Leak detection · GPU observability |
| Go-Live | 31 October 2026 — Anchor tenant reservations open now |
Rack layouts validated per chipset BoM around power draw, coolant manifold routing, weight loading, cable bend radius, and OEM service envelope.
Dedicated cages, private suites, logical segmentation with private interconnects. Biometric access, mantrap entry, and asset chain-of-custody control.
Redundant design across all critical MEP systems. 24×7 NOC/SOC. SLA parameters tuned to workload criticality, budget, and compliance tier.
Air cooling hits a physical limit at 15–20 kW per rack. Water has 3,000× the heat capacity of air by volume. By utilizing Liquid-to-Liquid CDU architecture, we remove heat 23× more effectively. This reclaims 25% of your power budget — previously wasted on hurricane-force fans — and gives it directly back to your GPUs for training throughput.
In high-density AI infrastructure, heat is the enemy of reliability. By maintaining a tight thermal differential, we significantly reduce junction temperatures, effectively doubling the operational lifespan of your $100M+ hardware investment. Lower operating temperatures translate directly to fewer thermal events, reduced throttling, and consistent peak compute performance.
GPU servers + OEM-specific cold plates
Supply/return coolant distribution per rack row
Liquid-to-liquid heat exchange + pumping
Chillers / dry coolers / heat rejection
Vera Rubin NVL72 mandates 100% liquid cooling — a requirement Cyfuture meets today. Whether deploying current Blackwell or planning for Vera Rubin H2 2026, our per-rack validated D2C infrastructure eliminates the infrastructure gap.
H100 / H200
B200‑based rack clusters
GB200 NVL72
GB300 NVL72
Vera Rubin NVL72 (H2 2026)
MI300X
MI350
Gaudi 2
Gaudi 3
Inference chips
Custom OEM
50 PFLOPS FP4
288 GB HBM4
NVLink 6 · 3.6 TB/s
H2 2026 availability
Every GPU generation raises the power and thermal bar. Cyfuture's liquid cool AI data center is the only Indian high-density colocation data center engineered for Blackwell, Grace Blackwell, and Vera Rubin — without a single infrastructure change between generations.
| Specification |
GENERATION 1 (NOW)
BlackwellB200 / B300 |
GENERATION 2 (NOW)
GraceBlackwellGB200 / GB300 NVL72 |
★ NEXT GEN (H2 2026)
Vera RubinRubin NVL72 |
|---|---|---|---|
| Architecture & Process | |||
| GPU Architecture | Blackwell (dual-die) 4nm TSMC |
Blackwell + Grace CPU 4nm / Arm Neoverse V2 |
Rubin GPU + Vera CPU 3nm TSMC (dual-die) |
| Transistors | 208 billion | 208B GPU + 144B Grace | 336B GPU + 227B Vera CPU |
| Compute Performance | |||
| FP4 Inference (dense) |
9 PFLOPS (B200)
15 PFLOPS (B300)
|
18 PFLOPS (GB200)
30 PFLOPS (GB300)
|
50 PFLOPS
5× over Blackwell
|
| FP4 Training | ~9–15 PFLOPS per GPU | 720 PFLOPS / NVL72 rack | 35 PFLOPS / GPU 3.5× over Blackwell |
| Rack-Scale Performance | ~720 PFLOPS (8-GPU DGX) | 1.1 ExaFLOPS (NVL72) GB300 NVL72 |
~3.6 ExaFLOPS (NVL72) Vera Rubin NVL72 |
| Memory | |||
| GPU Memory (per GPU) | 180 GB HBM3e (B200) 288 GB HBM3e (B300) |
384 GB (GB200) 576 GB (GB300 superchip) |
288 GB HBM4 Next-gen HBM4 stack |
| Memory Bandwidth | 7.7 TB/s (B200) 8 TB/s (B300) |
130 TB/s aggregate (NVL72) | 22 TB/s per GPU ~2.75× B300 |
| Memory Type | HBM3e (8 stacks) | HBM3e + 1.5TB LPDDR5x CPU | HBM4 (next-gen) + 1.5TB LPDDR5x (Vera CPU) |
| Power & Cooling Requirements | |||
| TDP per GPU | 700W–1,000W (B200) 1,100–1,400W (B300) |
~120 kW per rack (NVL72) |
~150–240 kW per rack Requires 240 kW+ infrastructure |
| Cooling Requirement | Liquid cooling strongly recommended | 100% liquid cooling required D2C mandatory for NVL72 |
100% LIQUID COOLING MANDATORY ✓ Cyfuture D2C = Fully Ready |
| Air Cooling Viable? | Possible at low density only | No — liquid only | Absolutely No No air-cooled config exists |
| Interconnect & Networking | |||
| NVLink Generation | NVLink 5 (1.8 TB/s per GPU) | NVLink 5 (1.8 TB/s per GPU) 130 TB/s rack aggregate |
NVLink 6 (3.6 TB/s per GPU) 260 TB/s rack aggregate |
| Network Fabric | 400 Gbps InfiniBand / Ethernet | 800 Gbps InfiniBand NDR ConnectX-8 SuperNIC |
800G–1.6T (ConnectX-9) Spectrum-X6 Ethernet Quantum-X800 InfiniBand |
| CPU Integration | Separate host CPU PCIe-attached |
Grace CPU integrated 7× lower CPU-GPU latency |
Vera CPU (88 cores) 1.8 TB/s NVLink-C2C 1.5 TB LPDDR5x |
| System Configuration (NVL72 / Rack-Scale) | |||
| GPUs per NVL72 Rack | 8 per DGX node (multi-node clusters) |
72 GPUs + 36 Grace CPUs in a single rack |
72 Rubin GPUs + 36 Vera CPUs One supercomputer per rack |
| GPUs to Train 10T MoE Model | Baseline reference | ~50% fewer than Hopper | ¼ the GPUs vs. Blackwell Same training time, same month |
| Inference Economics | |||
| Cost per Million Tokens | Baseline | ~30× lower than H100 (NVL72 rack-scale) |
10× lower than Blackwell NVIDIA official projection |
| Token Revenue per $100M | — | — | $5 Billion (Vera Rubin NVL144 CPX) |
| ️ Availability & Cyfuture Readiness | |||
| General Availability | Available Now | Available Now B200/B300 shipping H2 2025 |
H2 2026 In full production at NVIDIA |
| Cyfuture Infrastructure Status | ✓ FULLY READY | ✓ FULLY READY |
★ INFRASTRUCTURE READY 240 kW/rack D2C in place |
Unlike conventional facilities, Cyfuture's GPU colocation liquid cooling infrastructure was engineered from the ground up for Vera Rubin's 240 kW rack envelope. This is what AI ready colocation facilities actually look like — not retrofitted server rooms, but a liquid cooled colocation provider built from the slab up for 1,500W+ TDP chips. When your Vera Rubin allocation arrives in H2 2026, your India infrastructure will already be ready — with no gap, no wait, no compromise.
As high-density GPU colocation providers, Cyfuture's 10 MW liquid cooled AI data center is purpose-validated for both AMD CDNA 5 and NVIDIA Vera Rubin. Here is how the two architectures compare — and why your infrastructure choice matters more than your chipset choice.
| Specification |
AMD · CDNA 4 · NOW
MI350X / MI355X
Available Now
|
★ AMD NEXT-GEN
MI450 / MI455X
Helios Rack · H2 2026
|
★ HIGHEST PERFORMANCE
Vera Rubin NVL72
Rubin GPU + Vera CPU · H2 2026
|
|---|---|---|---|
| Architecture & Process | |||
| Architecture & Node | CDNA 4 TSMC 3nm (N3P + N6 base) |
CDNA 5 TSMC N2 (2nm-class) |
Rubin GPU + Vera CPU TSMC 3nm (dual-die) |
| Transistors | 185 billion | ~250 billion (est.) | 336B (GPU) + 227B (CPU) 563B total per superchip |
| Die Design | 3D MCM CDNA4 + N6 base |
Multi-chip CDNA 5 + EPYC Venice pairing |
Dual-die: Rubin GPU + Vera CPU NVLink-C2C connected |
| Compute Performance | |||
| FP4 Inference (per GPU) | 18.45 PFLOPS (MI350X) 20.1 PFLOPS (MI355X) |
40 PFLOPS 2× over MI355X |
50 PFLOPS ★ Highest single GPU |
| FP8 Inference (per GPU) | 9–10 PFLOPS | 20 PFLOPS | 35 PFLOPS (training) |
| Rack-Scale FP4 (72-GPU) | ~2.6 ExaFLOPS (128-GPU ORv3) |
2.9 ExaFLOPS Helios 72-GPU rack |
~3.6 ExaFLOPS ★ Highest rack performance |
| Memory & Bandwidth | |||
| Memory per GPU | 288 GB HBM3e | 432 GB HBM4 ★ Highest per-GPU memory |
288 GB HBM4 50% less than MI455X |
| Memory Bandwidth | 8 TB/s | 19.6 TB/s 2.45× over MI355X |
22 TB/s ★ Highest bandwidth |
| Total Rack Memory | 36 TB (128-GPU ORv3) |
31 TB (72-GPU Helios) |
~20 TB (72-GPU NVL72) |
| Power & Cooling (Why Liquid-Cooled Colocation Is Essential) | |||
| TDP per GPU | 1,000W (MI350X) 1,400W (MI355X) |
~1,500W (est.) Based on density estimates |
~1,500–1,600W (est.) Detailed TDP not yet disclosed |
| Rack Power Density | Up to ~120 kW (liquid) Air config at lower density |
~120 kW+ (Helios) Liquid only |
~180–240 kW (NVL72) Requires 240 kW+ infra |
| Cooling Requirement | Air (MI350X) or D2C liquid (MI355X) |
D2C liquid mandatory Quick-disconnect manifolds |
100% LIQUID MANDATORY ✓ No air config exists |
| Cyfuture D2C Readiness | ✓ Ready Now | ✓ Ready (H2 2026) | ★ Ready (H2 2026) |
| Interconnect & Scale-Up Fabric | |||
| Scale-Up Fabric | Infinity Fabric 8-GPU max scale-up |
UALink (open standard) First UALink-compatible GPU |
NVLink 6 — 3.6 TB/s per GPU Proprietary, mature ecosystem |
| Rack Aggregate Bandwidth | Limited (8-GPU scale-up) |
260 TB/s (UALink) 1.4 PB/s HBM4 aggregate |
260 TB/s NVLink 6 1.4 PB/s HBM4 aggregate |
| Scale-Out Networking | Ethernet Pensando Pollara 400G |
Ultra Ethernet (Vulcano 800G) Open standard (UEC) |
Quantum-X800 InfiniBand Spectrum-X6 · ConnectX-9 |
| CPU Pairing | AMD EPYC Separate host |
EPYC Venice (Zen 6) Integrated in Helios rack |
Vera CPU (88-core Arm Olympus) 1.8 TB/s NVLink-C2C · 1.5 TB LPDDR5x |
| ️ Rack Format & Ecosystem | |||
| Rack Format | OCP ORv3 (8-GPU nodes) |
Open Rack Wide v3 (Helios) 64, 72, or 128 GPUs |
MGX NVL72 rack 72 GPUs + 36 Vera CPUs |
| Software Ecosystem | ROCm (maturing) | ROCm + open fabric stack | CUDA (dominant) + CUDA-X Largest AI software ecosystem |
| Inference Cost vs. Blackwell | ~40% more tokens/$ vs NVIDIA (AMD claim) |
est. 2–3× improvement over MI355X |
10× lower cost/million tokens vs. Blackwell (NVIDIA official) |
| ️ Availability & Cyfuture Support | |||
| General Availability | Available Now | H2 2026 Oracle (50K MI450, Q3 2026) |
H2 2026 AWS, GCP, Azure, OCI, CoreWeave |
| Cyfuture Infrastructure | ✓ FULLY READY | ✓ READY H2 2026 | ★ READY H2 2026 240 kW/rack D2C in place |
Cyfuture's 10 MW direct-to-chip colocation facility is fully validated for both AMD CDNA 5 Helios and NVIDIA Vera Rubin NVL72. Whether your AI cluster data center hosting requirements lean toward AMD's open-standard UALink fabric or NVIDIA's CUDA-dominant ecosystem, our high-density GPU data center infrastructure handles both — with the same 240 kW/rack envelope, the same D2C cooling loops, and the same SEZ duty-free import advantage.
India's only data center built for Vera Rubin GPUs before they ship. While others prepare to retrofit, our 240 kW/rack direct-to-chip infrastructure is already validated — from power substation to cold plate manifold. The facility Blackwell runs in today, and Vera Rubin will run in tomorrow.
AI cluster data center hosting for LLM training, hyperscale inference, and RAG platforms. Each rack row is a dedicated liquid cooled AI data center pod — isolated, metered, and monitored per tenant.
A turnkey 10 MW data center requires 2N redundant power architecture — dual utility feeds, UPS-backed LV distribution, and A+B rack feeds. Every 10 MW data center space for lease includes dedicated power blocks.
Coolant Distribution Units running warm-water liquid cooling enable chiller-free economiser operation for the majority of annual hours — delivering a low PUE data center colocation environment below 1.3, versus the industry average of 1.57. Green data center liquid cooling is not a marketing claim — it is a physics outcome.
Finding colocation for 100 kW per rack is hard. Finding colocation with 800G InfiniBand NDR and RoCE v2 for GPU-to-GPU communication is harder. Cyfuture's AI ready colocation facilities ship with non-blocking spine-leaf fabric — purpose-built for the east-west traffic patterns of LLM training and distributed inference workloads.
Our structured onboarding ensures technical alignment before a single rack is moved. FAT/SAT and thermal acceptance testing guarantee your cluster is production-ready on day one.
Define GPU/accelerator BoM, OEM rack specifications, TDP profile, network fabric requirements, storage sizing, and compliance posture. Includes NDA execution.
POWER DENSITY · NETWORK FABRICRack drawings, power path design, cooling loops, CDU zone sizing and manifold routing, network fabric topology, and acceptance criteria definition per chipset BoM.
RACK LAYOUTS · CDU ZONESMW/rack reservation, pricing model, SLA parameters finalization, expansion rights, and SEZ operating structure and tax treatment review.
MW RESERVATION · SLA FINALIZATIONPer-rack validation of power draw, coolant flow, and TDP. Acceptance testing for each chipset configuration before production deployment.
COOLANT FLOW / TDP · ACCEPTANCE TESTINGFactory acceptance testing (FAT), integrated systems test (SAT), tenant acceptance sign-off, and production transition planning. Network topology validation and integrated systems go/no-go.
FAT / SAT · PRODUCTION TRANSITION24×7 NOC/SOC, DCIM reporting, managed AI infrastructure services, ongoing capacity planning, and GPU refresh cycle management through Cyfuture.ai platform.
24×7 NOC/SOC · MANAGED AI SERVICESCompute is only as fast as the network. We provide 400G/800G-ready InfiniBand and Ethernet topologies with low-latency switching designed to handle massive data throughput for RAG, multimodal training, and distributed inference.
Purpose-built AI infrastructure for enterprises, labs, and governments that need scale, sovereignty, and speed — all from India's first 10 MW liquid-cooled AI campus.
Train frontier LLMs, run RLHF, and generate synthetic data on dedicated high-density halls — without retrofitting constraints or cooling limits.
ExploreDeploy sovereign AI, private RAG, fraud analytics, and compliant inferencing on a private cloud with managed AI platform support.
ExploreRun inference APIs, agent platforms, chat/voice workloads, and embedding pipelines on GPU-as-a-Service or reserved clusters — scale as you grow.
ExploreHost language AI, citizen services, national datasets, and secure data platforms in India-hosted sovereign zones — fully managed and compliant.
ExploreEnable AI-driven diagnostics, drug discovery pipelines, and clinical data platforms with private, compliant infrastructure and data residency support.
ExplorePower rendering, digital twins, video AI, and high-throughput production pipelines with high-density compute and fast parallel storage.
ExploreEstablish regional AI PoPs, chip reference labs, and wholesale compute capacity with technical operations support and SEZ-structured import flexibility.
ExploreExpand India-market reach, reduce multicloud complexity, and deploy AI inference closer to users — with configurable cages and managed connectivity.
ExploreTenant-specific hall, cooling loop & network
Committed MW / rack blocks with expansion rights
Bare metal, Kubernetes, MLOps & inference layers
Colocation + managed cluster + Cyfuture AI stack
Dedicated halls or private suites for multi-node GPU cluster LLM training, RLHF, and multimodal model development at scale. InfiniBand fabric for tight GPU coupling across 8-GPU servers and NVL rack-scale platforms.
Sovereign AI infrastructure for private RAG, risk models, fraud analytics, and compliant inferencing. India data residency within SEZ boundaries. MeitY empanelled for government and regulated workloads.
GPU-as-a-Service for inference APIs, agent platforms, and embedding pipelines. Reserved clusters or serverless inference with 400G low-latency fabric for peak token throughput and minimal TTFT.
MeitY-empanelled sovereign AI zones for national language AI, citizen services, secure national datasets, and government AI programs with full India data residency and audit governance.
Wholesale capacity blocks, AI PoP deployments, and chip reference lab environments for regional cloud providers and OEM partners entering India's AI infrastructure market with SEZ advantages.
High-density compute with parallel file system storage for media rendering, digital twins, video AI, and simulation workloads requiring sustained GPU throughput and high-bandwidth I/O.
The SEZ location provides export-oriented AI operators with significant trade and operating benefits — subject to approvals and compliance.
Infrastructure, equipment, and goods for authorized operations may be imported duty-free, subject to approvals.
SEZs are treated as outside India's customs territory for authorized operations — improving hardware movement flexibility.
SEZ scheme features no import licence requirement for authorized imports, reducing friction for global hardware shipments.
Supplies to SEZ units are zero-rated under IGST, subject to GST law and appropriate documentation.
Central and state-level approval processes supported through the SEZ framework for streamlined onboarding.
Units are expected to achieve positive Net Foreign Exchange cumulatively over five years from commencement of services.
Multi-layer perimeter, mantrap entry, biometric access, 24×7 CCTV surveillance, visitor management, and asset chain-of-custody logging per tenant boundary.
Network segmentation, tenant firewalls, private links, IAM, secrets management, secure remote access, and continuous vulnerability management.
Encryption at rest and in transit. Key management options. Secure backup and deletion workflows aligned to BFSI, healthcare, and government data residency requirements.
ISO 27001 / SOC-style controls. Audit support packages. MeitY empanelment for government workloads. Data residency documentation for regulated sectors.
Real-time dashboards for power draw, cooling metrics, GPU utilization, incident tracking, SLA performance, and sustainability telemetry — per-tenant portals.
Round-the-clock operations center monitoring. Change management, incident response, capacity planning, and preventive maintenance for AI infrastructure criticality.
Once deployed, scale through reserved capacity rights and expansion-ready modular power blocks. Four deployment models designed for every stage of AI infrastructure maturity.
Tenant-specific hall with dedicated cooling loop, custom network architecture, and purpose-built MEP systems configured around your GPU BoM.
MW or rack-block reservations with contractual expansion rights. Predictable capacity without greenfield lead-time risk. Lock in 2026 pricing now.
Bare metal GPU + managed Kubernetes + MLOps + inference endpoints. Cyfuture.ai software stack on dedicated hardware in the SEZ AI data center.
Colocation infrastructure plus managed GPU cluster tier plus the Cyfuture.ai software stack — for mixed workloads requiring both physical control and platform services.
For hyperscalers, sovereign AI programmes, national cloud initiatives, and global AI labs that need exclusive control of an entire liquid-cooled 10 MW campus — Cyfuture offers a single-tenant, build-to-suit whole-facility lease with full operational flexibility and SEZ import advantages.
Government-mandated AI infrastructure requiring data residency, air-gap capability, and exclusive physical control. MeitY empanelled, India-hosted, SEZ-structured.
International cloud providers seeking a fully liquid-cooled, Vera Rubin-ready India PoP without the 3-year lead time of building their own facility.
Labs training 100B+ parameter models requiring contiguous GPU clusters at 10 MW+ scale — with the network fabric, storage throughput, and cooling density to match.
MSPs and GPU cloud operators looking to white-label an entire 10 MW liquid-cooled AI campus as their own India infrastructure product — with Cyfuture managing MEP, security, and compliance.
If you are evaluating the entire 10 MW campus as a single-tenant lease — for a national AI programme, hyperscale India deployment, or sovereign GPU cloud — our team is ready for a confidential technical and commercial discussion.
NVIDIA's roadmap is relentless. Every GPU generation raises the thermal and power bar. While others scramble to retrofit their air cooling vs liquid cooling data center infrastructure, Cyfuture's direct-to-chip colocation facility was engineered from day one to sit ahead of the curve — no gaps, no delays, no compromises.
B200 / B300 · HGX / DGX / NVL formats
GB200 / GB300 · NVL72 rack-scale
NVL72 · 72 Rubin GPUs + 36 Vera CPUs
From a single rack to the entire 10 MW campus — Cyfuture's liquid-cooled AI data center is open for reservation. Vera Rubin NVL72 ships H2 2026 — secure your capacity now so your infrastructure is already running when your allocation arrives.
Total IT Capacity
Max Rack Density
Token Cost Reduction (Rubin)
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