In one of Asia’s largest frontier compute deployments, Greenko-promoted AM Intelligence has placed a binding order for 9,000 next-generation NVIDIA Vera Rubin GPUs for its flagship Hyderabad AI Factory, anchoring a massive $8 billion, 1 GW “Electron-to-Token” global compute ecosystem.
Executive Summary & Key Transaction Metrics
AM Intelligence (AMI), the frontier artificial intelligence infrastructure platform established by the founders of renewable energy giant Greenko, has finalized a binding procurement order with NVIDIA Corporation for 9,000 next-generation Vera Rubin GPUs. Scheduled for commercial delivery and deployment in the first quarter of 2027, the graphics processing units will power AMI’s 30-megawatt (MW) high-density AI Factory situated in Hyderabad, Telangana.
| Parameter | Details |
| Core Hardware Purchase | 9,000 NVIDIA Rubin GPUs (Vera Rubin NVL72 Architecture) |
| Primary Facility Location | Hyderabad AI Factory, Telangana, India |
| Delivery & Commissioning | First Quarter (Q1) 2027 |
| Initial Facility Scale | 30 Megawatts (MW) high-density liquid-cooled campus |
| Theoretical Compute Peak | ~450 ExaFLOPS (NVFP4 Precision Inference Performance) |
| Near-Term Capex Outlay | Over $8 Billion (USD) backing 200 MW rollout |
| Global CaaS Target | 1 Gigawatt (GW) across India, USA, Finland, & Malaysia |
| Total Power AI Pipeline | 5 Gigawatts (GW) of powered AI campuses worldwide |
| Core Operational Thesis | “Electron-to-Token” vertically integrated power & compute |
This procurement represents one of the earliest and largest commercial commitments in Asia for NVIDIA’s post-Blackwell architecture. The deployment establishes Hyderabad as a premier global hub for frontier artificial intelligence training, massive trillion-parameter foundation model inference, and autonomous agentic workflows.
Underpinning this compute deployment is an aggressive $8 billion near-term capital expenditure plan designed to deliver 200 MW of high-density capacity, eventually scaling to 1 GW of Compute-as-a-Service (CaaS) across facilities in India, the United States, Finland, and Malaysia, within a broader 5 GW global energy-compute pipeline.
The Strategic Procurement: 9,000 NVIDIA Vera Rubin GPUs
The agreement centers on NVIDIA’s Vera Rubin platform, the successor to the Grace Blackwell generation. The order covers 9,000 Rubin GPUs configured inside Vera Rubin NVL72 rack-scale systems.
- Power & Cooling Infrastructure: Direct-to-Chip liquid cooling system paired with a 30 MW dedicated substation.
- Rubin GPU Compute Fabric: Multi-cluster Vera Rubin NVL72 setup integrating 36 Vera CPUs and 72 GPUs per rack with HBM4 memory subsystems.
- Interconnect & Storage Fabric: Ultra-dense RoCE network mesh backed by an exabyte-scale NVMe storage tier, delivering ~450 ExaFLOPS of NVFP4 computing performance.
Architectural Details of the Vera Rubin Architecture
Each standard NVIDIA Vera Rubin NVL72 system consolidates 72 Rubin GPUs alongside 36 custom Vera central processing units (CPUs) inside a single, liquid-cooled, exascale-ready rack architecture.
- Advanced High-Bandwidth Memory (HBM4): The Rubin platform integrates next-generation HBM4 memory stacks, expanding memory bandwidth per GPU to overcome the “memory wall” that restricts training and execution speeds on trillion-parameter frontier networks.
- NVFP4 Precision Processing: The Hyderabad deployment is specifically optimized for low-precision NVFP4 (4-bit floating-point) inference formats. According to AM Intelligence architectural benchmarks, the 9,000-GPU installation will deliver approximately 450 exaFLOPS of aggregate NVFP4 computing performance.
- Agentic AI Throughput: NVIDIA’s platform engineering indicates that Vera Rubin systems provide up to a 10x throughput multiplier for agentic AI applications—autonomous systems that execute complex reasoning, planning, and tool-calling loops—compared to previous hardware iterations.
- Ultra-Low Inference Token Costs: The hardware architecture and dense interconnects are engineered to slash the cost of token serving by up to 10x compared to the NVIDIA Grace Blackwell generation, giving enterprises, researchers, and startups operating in India access to high-efficiency compute economics.
| Architectural Metric | Hopper (H100/H200) | Blackwell (B200) | Vera Rubin (NVL72) |
| Memory Technology | HBM3 / HBM3e | HBM3e | Next-Gen HBM4 |
| Primary Inference Format | FP8 / FP16 | FP4 / FP8 | Advanced NVFP4 |
| Scale-up Rack Integration | 8-GPU Baseboards | NVL72 (72 GPUs) | NVL72 (72 GPUs+CPUs) |
| Cooling Architecture | Air / Hybrid Liquid | Direct Liquid | 100% Direct Liquid |
| Target Workload Profile | Transformer Training | LLM Inference/MoE | Trillion-Param/Agentic |
| Token Efficiency Ratio | Baseline (1x) | ~3x – 4x | Up to 10x |
The $8 Billion Capital Expansion and 1 GW CaaS Vision
The deployment of 9,000 GPUs at the 30 MW Hyderabad facility serves as the opening phase of AM Intelligence’s broader capital program. The company has outlined a phased rollout backed by an initial capital expenditure budget exceeding $8 billion:
- Phase 1 (Hyderabad Launch – Q1 2027): 30 MW High-Density AI Factory equipped with 9,000 Rubin GPUs delivering 450 ExaFLOPS NVFP4 compute.
- Phase 2 (Near-Term Multi-Region Rollout): Over $8 billion in capital expenditure deploying a 200 MW active compute pipeline across strategic continental hubs.
- Phase 3 (Global Compute-as-a-Service Platform): Reaching 1 GW of operating compute capacity distributed across India, the United States, Finland, and Malaysia.
- Phase 4 (Full-Scale Power-to-Intelligence Ecosystem): Operating a 5 GW global backbone of powered AI data center campuses with integrated liquid cooling.
1. Near-Term 200 MW Rollout
AMI plans to commercialize an initial 200 MW of specialized AI compute capacity in the near term. This initial tranche involves an investment of over $8 billion dedicated to land acquisition, substations, direct-to-chip liquid cooling installations, ultra-dense RoCE (RDMA over Converged Ethernet) networking fabrics, high-throughput storage, and silicon procurement.
2. Global Footprint Across Four Continents
AMI’s 1 GW Compute-as-a-Service strategy is designed to balance geographic latency, sovereign regulatory compliance, and grid reliability:
| Node / Country | Power Baseline | Primary Strategic Focus |
| Hyderabad, India | 30 MW (Initial) | Frontier model training & APAC Sovereign AI |
| United States Node | Multi-Site Build | Hyperscale neo-cloud & Frontier AI Labs |
| Finland Campus | Hydro/Wind Grid | Net-zero European enterprise & sovereign AI |
| Malaysia Campus | Dense APAC Fabric | Low-latency inference & regional neo-cloud |
The “Electron-to-Token” Strategy: Solving the Power Bottleneck
The primary bottleneck in global artificial intelligence infrastructure has shifted from silicon supply to electrical power availability, thermal management, and grid interconnects.
AM Intelligence is sponsored by the founders of Greenko, one of India’s largest renewable energy conglomerates. Over the past two decades, Greenko built a clean energy footprint encompassing over 7.5 GW of operating wind, solar, and hydro assets, along with multi-gigawatt-hour pumped-storage projects (PSP) capable of delivering round-the-clock (RTC) green power.
- Raw Power Generation: Solar arrays, wind farms, and pumped storage hydropower supplying round-the-clock, carbon-free energy.
- High-Voltage Transmission: Dedicated substation infrastructure and microgrid power conditioning for extreme reliability.
- High-Density Data Center Facilities: 5 GW global pipeline operating direct liquid cooling designed for 100+ kW rack densities.
- Next-Generation Compute Fabric: NVIDIA Vera Rubin NVL72 clusters connected via ultra-high-throughput RoCE networks.
- Token Generation & Monetization: Compute-as-a-Service, trillion-parameter inference endpoints, and agentic API hosting.
Chairman Anil Chalamalasetty’s Industry Thesis
Anil Chalamalasetty, Chairman of AM Intelligence, highlighted the company’s core strategic shift during the procurement announcement:
“For over two decades, we have focused on transforming electrons into value — addressing critical and emerging societal needs. Today, the electron-to-token opportunity allows us to take this capability further, converting power infrastructure into frontier AI compute at scale. Bringing latest generation NVIDIA Vera Rubin to India marks the next step in this journey and establishes AMI at the forefront of the emerging AI electron-to-token economy.”
By co-locating ultra-dense data centers with dedicated, dispatchable clean energy infrastructure, AMI sidesteps the multi-year grid-interconnect backlogs that currently delay data center projects in Western markets.
Engineering the Hyderabad AI Factory: Liquid Cooling and Networking
Operating thousands of advanced GPUs requires specialized facilities capable of managing high power draws and thermal outputs. Standard air-cooled server rooms, designed for 10 kW to 20 kW per rack, cannot support next-generation AI silicon.
| Facility Parameter | Specification & Implementation |
| Power Capacity | 30 Megawatts (MW) dedicated power feed |
| Rack Power Density | 100 kW to 130 kW+ per rack footprint |
| Thermal Management | 100% Direct-to-Chip (D2C) liquid cooling distribution loops |
| Networking Protocol | Ultra-low latency RoCE (RDMA over Converged Ethernet) fabric |
| Storage Tiering | Multi-petabyte NVMe-oF low-latency caching storage |
| Silicon Generation | NVIDIA Vera Rubin NVL72 with NVLink switch fabric |
| Compute Output | ~450 ExaFLOPS NVFP4 low-precision inference capacity |
Direct Liquid Cooling (DLC) Implementation
NVIDIA’s Vera Rubin NVL72 racks produce substantial thermal output, often exceeding 100 kW to 130 kW per operational rack unit. The Hyderabad AI Factory utilizes direct-to-chip liquid cooling systems where treated coolant circulates directly over micro-channel cold plates mounted to the Vera CPUs, Rubin GPUs, and NVLink switches.
The coolant distribution unit (CDU) constantly monitors pressure, flow rates, and temperatures, transferring heated fluid to heat rejection exchangers before recirculating the cooled liquid in a closed loop. This design maintains low thermal resistance, prevents hardware throttling, and lowers the facility’s Power Usage Effectiveness (PUE) ratio.
Ultra-High-Bandwidth Network Fabric
To train large AI models across 9,000 interconnected GPUs without latency degradation, AMI is deploying high-throughput, low-latency networking fabrics using RDMA over Converged Ethernet (RoCE) alongside NVIDIA NVLink multi-tier switching. This topology allows the entire cluster to function as a unified, warehouse-scale computing engine.
Market Implications for India’s AI Ecosystem and the Global Neo-Cloud Market
AMI’s hardware procurement and infrastructure deployment reshape the competitive dynamics of both the domestic and international compute markets.
| Market Segment | Key Operational Need | AMI Strategic Solution |
| Global Hyperscalers | Overflow capacity & APAC PoPs | 200 MW – 1 GW CaaS capacity |
| Sovereign AI (India) | Data residency & local compute | Domestic NVL72 supercluster |
| Frontier AI Labs | Trillion-parameter training | 450 ExaFLOPS NVFP4 compute |
| AI-Native Startups | Low-cost inference token APIs | 10x cheaper token serving |
| Enterprise Neo-Clouds | Bare-metal GPU virtualization | Liquid-cooled Rubin clusters |
1. Strengthening Sovereign AI and the IndiaAI Mission
A key challenge for Indian AI researchers, startups, and sovereign initiatives has been the limited domestic availability of large-scale GPU clusters. By deploying 9,000 Rubin GPUs in Hyderabad, AMI provides the computing capacity needed to train Indic foundation models locally, ensuring critical data remains within national borders while lowering dependence on overseas cloud instances.
2. Competing in the Global Neo-Cloud Sector
The global rise of specialized “neo-cloud” providers—cloud companies focused purely on high-performance AI workloads rather than traditional enterprise hosting—has driven heavy demand for turnkey GPU clusters. With 1 GW of planned Compute-as-a-Service capacity across India, North America, Europe, and Southeast Asia, AM Intelligence is positioning itself to compete with international AI cloud operators.
Technical Comparison: Compute Architectures
The architectural leap from current-generation systems to the Vera Rubin platform reflects the rapid evolution of artificial intelligence hardware.
| Parameter | NVIDIA H100 SXM5 | NVIDIA B200 NVL72 | Vera Rubin NVL72 |
| GPU Transistor Density | ~80 Billion | ~208 Billion | Multi-Die Chiplet |
| Memory Packaging | HBM3 (80 GB) | HBM3e (192 GB) | Next-Gen HBM4 |
| Compute Precision Standard | FP8 / FP16 | FP4 / FP8 | Optimized NVFP4 |
| Interconnect Bandwidth | 900 GB/s NVLink 4 | 1.8 TB/s NVLink 5 | Next-Gen NVLink |
| Native CPU Integration | Host x86 Servers | Grace ARM CPU Pair | Vera Custom CPU |
| Maximum Rack Density | ~40 kW | ~120 kW | ~130 kW+ |
| Primary Thermal Method | Air Cooled / Liquid | Direct Liquid (DLC) | Direct Liquid (DLC) |
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Frequently Asked Questions (FAQ)
What specific hardware did AM Intelligence order from NVIDIA?
AM Intelligence placed a binding order for 9,000 NVIDIA Rubin GPUs configured within Vera Rubin NVL72 rack-scale systems, which combine Rubin GPUs with Vera CPUs, next-generation HBM4 memory, and NVFP4 precision formats.
Where will these GPUs be installed, and when will they go live?
The 9,000 GPUs will be deployed at AMI’s 30 MW AI Factory in Hyderabad, Telangana, India, with delivery and operational commissioning scheduled for the first quarter (Q1) of 2027.
What is the financial scale and total capacity of the rollout?
The deployment forms the first phase of an $8 billion+ capital expenditure plan to bring 200 MW of high-density AI compute online in the near term. This fits into a broader roadmap targeting 1 GW of Compute-as-a-Service (CaaS) capacity and 5 GW of powered AI data center campuses globally.
What is the “Electron-to-Token” concept?
Coined by AMI Chairman Anil Chalamalasetty, “Electron-to-Token” refers to vertically integrating clean energy generation (electrons) with AI data centers and supercomputers to produce artificial intelligence inferences and model outputs (tokens) at scale.
Which countries are included in AMI’s 1 GW global rollout?
AMI’s planned 1 GW Compute-as-a-Service footprint covers four countries: India, the United States, Finland, and Malaysia.
Strategic Impact Summary
- Advanced Silicon: First large-scale deployment of NVIDIA Vera Rubin in Asia.
- Scale & Capital: 9,000 GPUs backed by an $8B+ capex and 200 MW near-term plan.
- Energy Integration: Leveraging 5 GW of clean power to eliminate grid bottlenecks.
- Sovereign AI Boost: High-density local compute for India’s digital ecosystem.
- Global Ambition: 1 GW CaaS footprint across India, USA, Finland, and Malaysia.
AM Intelligence’s procurement of 9,000 NVIDIA Vera Rubin GPUs marks a major milestone in India’s technology and computing landscape. By coupling large-scale hardware deployment with dedicated clean power and liquid cooling infrastructure, the Hyderabad AI Factory establishes a resilient, vertically integrated model for the global AI compute economy.
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