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Sovereign AI infrastructure

End-to-end AI compute under one sovereign umbrella. Designed here. Manufactured here. Supported here.

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SLA-driven. Not ticket-driven.

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ISO 9001. PLI 2.0. SOP-led manufacturing. The systems behind every device we ship.

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AI Infrastructure Solutions for Private AI Factory Deployments

22 fixed reference configurations across AI-GPU compute, NVMe storage, lossless fabric, and ops packs — quote-ready models that compose into complete AI-POD rack solutions. From inference to training, every component is pre-validated and price-locked.

AI-POD System SKUs

Complete 1-Rack AI Factory Solutions with Fixed BOM Mapping

Pre-validated AI-POD configurations for GenAI inference, vision AI, and training — each POD is a complete rack solution with compute, storage, fabric, and ops bundled into a single quote-ready package.

AI-POD-7101
RDP GenAI Inference Pod (1 Rack)
Compute
3× AI-GPU-72201 (4-GPU Inference Core | Intel)
Hot Storage
1× AI-STG-6301B (Hot NVMe HA | Core)
Fabric
1× AI-FAB-6401 (100GbE Lossless HA Pair)
Ops
AI-OPS-6501 + 6502 Recommended
Best For
Private copilots, RAG, embeddings, enterprise inference services (CIOs, GCCs, BFSI, manufacturing HQ)
AI-POD-7102
RDP Vision AI Pod (1 Rack)
Compute
4× AI-GPU-72101 (4-GPU Inference Entry | Intel)
Retention
1× AI-STG-6302A (Capacity + Throughput | Entry)
Optional Hot
+ AI-STG-6301A (Recommended for Faster Indexing)
Fabric
1× AI-FAB-6401 (100GbE Lossless HA Pair)
Best For
Video analytics, factory safety, quality inspection, surveillance intelligence (manufacturing plants, warehouses, campuses)
AI-POD-7103
RDP Training / Fine-tuning Pod (1 Rack)
Compute
2× AI-GPU-75101 (8-GPU Training Entry | Intel)
Hot Storage
1× AI-STG-6301B (Hot NVMe HA | Core)
Dataset Lake
+ AI-STG-6302A/6302B (Recommended)
Fabric
1× AI-FAB-6402 (200GbE Lossless HA Pair)
Best For
Fine-tuning, training runs, batch AI pipelines, model iteration (ISVs, product engineering teams, research units)
AI-GPU (8 Models)

Fixed Reference Configurations for AI-GPU Compute Nodes

Price-only decisions for CIOs — every configuration is pre-validated with specific CPU, GPU, memory, and networking components. Intel and AMD variants for every tier.

A) 4-GPU Inference Entry (48GB VRAM Class)
AI-GPU-72101
4-GPU Inference Node (Entry | Intel)
Chassis
4U GPU Server, Front-to-Back Airflow
CPU
2× Intel Xeon Gold 6430 (32C Each)
Memory
512GB DDR5 ECC (16×32GB, 4800 MT/s)
GPU
4× NVIDIA L40S 48GB (PCIe Gen5 x16)
Storage
2× 1.92TB SATA SSD (RAID-1) + 2× 3.84TB NVMe
Network
1× ConnectX-6 Dx Dual-Port 100GbE
Power
2× 3000W Titanium Redundant (~2.5–3.5 kW)
Best For
RAG, copilots, embeddings, departmental inference, PoCs — works standalone or inside AI-POD-7101/7102
AI-GPU-72102
4-GPU Inference Node (Entry | AMD)
Chassis
4U GPU Server, Front-to-Back Airflow
CPU
2× AMD EPYC 9354 (32C Each)
Memory
512GB DDR5 ECC (16×32GB, 4800 MT/s)
GPU
4× NVIDIA L40S 48GB (PCIe Gen5 x16)
Storage
2× 1.92TB SATA SSD (RAID-1) + 2× 3.84TB NVMe
Network
1× ConnectX-6 Dx Dual-Port 100GbE
Power
2× 3000W Titanium Redundant (~2.5–3.5 kW)
Best For
Same as 72101 with AMD EPYC platform — for AMD-only accounts or preference
B) 4-GPU Inference Core (80GB VRAM Class)
AI-GPU-72201
4-GPU Inference Node (Core | Intel)
Chassis
4U GPU Server, Redundant Fans + PSUs
CPU
2× Intel Xeon Gold 6454S (32C Each)
Memory
768GB DDR5 ECC (12×64GB, 4800 MT/s)
GPU
4× NVIDIA H100 80GB (PCIe Gen5 x16)
Storage
2× 1.92TB SATA SSD (RAID-1) + 4× 3.84TB NVMe
Network
1× ConnectX-6 Dx Dual-Port 100GbE
Power
2× 3000W Titanium Redundant (~3.0–4.5 kW)
Best For
Enterprise concurrency, heavier RAG, multi-tenant inference, higher VRAM workloads — default for AI-POD-7101
AI-GPU-72202
4-GPU Inference Node (Core | AMD)
Chassis
4U GPU Server, Redundant Fans + PSUs
CPU
2× AMD EPYC 9454 (48C Each)
Memory
768GB DDR5 ECC (12×64GB, 4800 MT/s)
GPU
4× NVIDIA H100 80GB (PCIe Gen5 x16)
Storage
2× 1.92TB SATA SSD (RAID-1) + 4× 3.84TB NVMe
Network
1× ConnectX-6 Dx Dual-Port 100GbE
Power
2× 3000W Titanium Redundant (~3.0–4.5 kW)
Best For
Same as 72201 with AMD EPYC platform — higher core count option
C) 8-GPU Training Entry (80GB Training Class)
AI-GPU-75101
8-GPU Training Node (Entry | Intel)
Chassis
8U HGX-Class 8-GPU Platform
CPU
2× Intel Xeon Platinum 8480+ (56C Each)
Memory
1TB DDR5 ECC (16×64GB, 4800 MT/s)
GPU
NVIDIA HGX H100 (SXM) — 8× 80GB
Interconnect
NVLink / NVSwitch (HGX Platform)
Storage
2× 1.92TB SATA SSD (RAID-1) + 4× 3.84TB NVMe
Network
1× ConnectX-7 Dual-Port 200GbE
Power
4× 3000W Titanium Redundant (~8–12 kW)
Best For
Fine-tuning, training runs, batch AI pipelines, ISVs/R&D teams starting training — starter node for AI-POD-7103
AI-GPU-75102
8-GPU Training Node (Entry | AMD)
Chassis
8U HGX-Class 8-GPU Platform
CPU
2× AMD EPYC 9654 (96C Each)
Memory
1TB DDR5 ECC (16×64GB, 4800 MT/s)
GPU
NVIDIA HGX H100 (SXM) — 8× 80GB
Interconnect
NVLink / NVSwitch (HGX Platform)
Storage
2× 1.92TB SATA SSD (RAID-1) + 4× 3.84TB NVMe
Network
1× ConnectX-7 Dual-Port 200GbE
Power
4× 3000W Titanium Redundant (~8–12 kW)
Best For
Same as 75101 with AMD EPYC platform — higher core count for parallel workloads
D) 8-GPU Training Pro (High-Memory Training Class)
AI-GPU-75301
8-GPU Training Node (Pro | Intel)
Chassis
8U HGX-Class Platform
CPU
2× Intel Xeon Platinum 8480+ (56C Each)
Memory
2TB DDR5 ECC (16×128GB, 4800 MT/s)
GPU
NVIDIA HGX H200 (SXM) — 8× 141GB
Interconnect
NVLink / NVSwitch (HGX Platform)
Storage
2× 1.92TB SATA SSD (RAID-1) + 8× 3.84TB NVMe
Network
1× ConnectX-7 Dual-Port 200GbE
Power
4× 3000W Titanium Redundant (~10–14 kW)
Best For
Heavier fine-tuning, larger models, longer sustained training, faster checkpointing — Pro/core node for AI-POD-7103
AI-GPU-75302
8-GPU Training Node (Pro | AMD)
Chassis
8U HGX-Class Platform
CPU
2× AMD EPYC 9654 (96C Each)
Memory
2TB DDR5 ECC (16×128GB, 4800 MT/s)
GPU
NVIDIA HGX H200 (SXM) — 8× 141GB
Interconnect
NVLink / NVSwitch (HGX Platform)
Storage
2× 1.92TB SATA SSD (RAID-1) + 8× 3.84TB NVMe
Network
1× ConnectX-7 Dual-Port 200GbE
Power
4× 3000W Titanium Redundant (~10–14 kW)
Best For
Same as 75301 with AMD EPYC platform — maximum CPU cores for data-heavy training
AI-STG (4 Models)

Hot NVMe HA Storage and Capacity Tiers for AI Workloads

Purpose-built storage blocks for AI: NVMe hot tier for vector DBs, checkpoints, and active datasets; capacity tier for dataset lakes, vision retention, and long-term replay.

1) AI Hot Tier (Vector DB, Checkpoints, Active Datasets)
AI-STG-6301A
Hot NVMe HA Storage (Entry | Dual-Controller | 2U)
Form Factor
2U, 24× U.2 NVMe Bays (Front)
Controllers
Dual Active-Active (HA), Hot-Swappable
Media
24× 3.84TB U.2 NVMe (Enterprise)
Capacity
~92TB Raw / ~60–70TB Usable
Connectivity
8× 100GbE Ports Total (QSFP28)
Protocols
NFSv3/v4, SMB, iSCSI, NVMe/TCP
Features
Thin Provisioning, Snapshots, QoS, HA Failover
Power
Dual Redundant PSUs (~800W–1.6kW)
Best For
RAG/vector DB, hot corp store, model cache, checkpointing for smaller training pods — default hot tier for AI-POD-7101
AI-STG-6301B
Hot NVMe HA Storage (Core | Dual-Controller | 2U)
Form Factor
2U, 24× U.2 NVMe Bays (Front)
Controllers
Dual Active-Active (HA), Hot-Swappable
Media
24× 7.68TB U.2 NVMe (Enterprise)
Capacity
~184TB Raw / ~120–140TB Usable
Connectivity
8× 100GbE Ports Total (QSFP28)
Protocols
NFSv3/v4, SMB, iSCSI, NVMe/TCP
Features
Thin Provisioning, Snapshots, QoS, HA Failover
Power
Dual Redundant PSUs (~800W–1.6kW)
Best For
Larger RAG corp, bigger vector stores, faster checkpoint bursts, multi-team AI pods — core hot tier for AI-POD-7101, default for AI-POD-7103
2) AI Capacity Tier (Dataset Lake, Vision Retention, Long Retention)
AI-STG-6302A
Capacity + Throughput Storage (Entry | 4U 60-Bay | HA)
Form Factor
4U, 60× LFF Bays (3.5")
Controllers
Dual Controllers (HA), Hot-Swappable
Media
60× 18TB NL-SAS HDD + 4× 3.84TB SSD Cache
Capacity
~1080TB Raw / ~750–850TB Usable
Connectivity
8× 25GbE or 4× 100GbE (Select at Order)
Protocols
NFSv3/v4, SMB (Optional), S3 (Optional)
Features
Snapshots, Quotas, Tiering, Replication-Ready
Power
Dual Redundant PSUs (~1.0–2.5kW)
Best For
Vision/video retention, AI dataset lake, long retention + replay — default retention tier for AI-POD-7102, optional dataset lake for AI-POD-7103
AI-STG-6302B
Capacity + Throughput Storage (Core | 4U + Expansion | HA)
System
Base (4U 60-Bay) + 1× Expansion (4U 60-Bay)
Controllers
Dual Controllers (HA), Hot-Swappable
Media
120× 18TB NL-SAS HDD + 4× 3.84TB SSD Cache
Capacity
~2160TB Raw / ~1.5–1.7PB Usable
Connectivity
8× 25GbE or 4× 100GbE (Select at Order)
Protocols
NFSv3/v4, SMB (Optional), S3 (Optional)
Features
Same as 6302A, Higher Capacity + Better Parallelism
Power
Dual Redundant PSUs (~1.0–2.5kW)
Best For
Bigger multi-site retention, larger dataset lake, "grow without redesign" — core retention for larger AI-POD-7102, recommended dataset lake for AI-POD-7103
AI-FAB (Fabric Options)

Lossless Fabric Infrastructure for AI Workloads

Pod-scale and training-scale lossless Ethernet fabric with PFC/ECN tuning, QoS templates, and telemetry — purpose-built for GPU-to-storage east–west traffic.

AI-FAB-6401
100GbE Lossless Fabric (HA Pair | Pod Scale)
Design
2× ToR Switches (HA Pair), MLAG/vPC
Ports
32×100GbE QSFP28 Per Switch
Features
Lossless Ethernet (PFC/ECN), LACP/MLAG, QoS
Telemetry
Port Health, Link Flaps, Error Counters
Cabling
DAC/Optics for Up to 4× GPU + 1× STG
Mgmt
1× Mgmt Port Per Switch, RBAC, Audit Logs
Best For
Stable throughput between GPU nodes + hot storage for inference and vision pods — default for AI-POD-7101 and AI-POD-7102
AI-FAB-6402
200GbE Lossless Fabric (HA Pair | Training Scale)
Design
2× ToR Switches (HA Pair), MLAG/vPC
Ports
32×200GbE QSFP112 Per Switch
Features
Lossless Tuning (PFC/ECN), QoS for Storage/Checkpoint
Telemetry
Fabric Health Baselines
Cabling
For Up to 2× 8-GPU Training + 1× Hot NVMe
Mgmt
1× Mgmt Port Per Switch, RBAC, Audit Logs
Best For
Training pods need higher east–west throughput and checkpoint bursts — default fabric for AI-POD-7103
AI-FAB-6403
400GbE Spine Option (Multi-Rack Scale | Optional)
Design
2× Spine Switches (400GbE)
Integration
Integrates with 6401/6402 as Leaf Layer
Use Case
When Customer Scales to 2+ Racks, Leaf-Spine
Best For
Multi-rack scale (3–10 racks), no redesign later — optional for future expansion
AI-OPS (Ops / Acceptance Options)

Deployment Readiness & Lifecycle Management for AI Infrastructure

Factory acceptance testing, day-2 operations runbooks, managed monitoring, and annual health checks — operational confidence for every AI-POD and standalone GPU deployment.

AI-OPS-6501
AI Factory Acceptance Pack (Mandatory)
Includes
Rack Installation Validation: Power, PDU, Thermal Mapping
GPU Burn-in
Multi-Hour GPU Stress + PCIe Checks + Error Logs
Storage
Throughput + Latency Baseline + Failover Test
Fabric
Lossless Profile Applied, Link Tests, Redundancy Failover
Inventory
Serials, MACs, Firmware Versions Captured
Outputs
Acceptance Report + Baseline Performance + Handover Pack
Best For
"Go-live confidence" with validation and sign-off — applies to every AI-POD, recommended for standalone AI-GPU
AI-OPS-6502
Day-2 Ops Runbook Pack (Recommended)
Monitoring
GPU Health, Temps, Fan, Power, ECC, NVMe Wear, Fabric
Alerts
Thresholds + Escalation Matrix
Baselines
Driver/Firmware Baseline (Known-Good Versions)
Patch
Quarterly Recommended + Rollback Method
Spares
PSUs, Fans, NVMe, Optics Recommendation
Outputs
Runbook PDF + Monitoring Checklist + Escalation Contacts
Best For
Ensure customer can run AI factory without chaos — recommended for all AI-PODs
AI-OPS-6503
Managed Monitoring + Patch Cadence (Optional)
Includes
Monthly Health Dashboard (GPU, Storage, Fabric)
Patch
Planning + Firmware/Driver Upgrade Support
Incident
Triage Support (Within SLA)
Best For
RDP + SI can deliver "light managed services" around the pod — CIOs who want single accountability without full managed hosting
AI-OPS-6504
Annual Health Check + Firmware Baseline (Optional)
Includes
Full Diagnostics + Firmware Baseline Refresh
Assessment
Performance Drift Assessment
Checks
Thermal/Power Check + Recommendations
Best For
Yearly preventive maintenance + trust — optional for customers who want ongoing assurance

Need a Custom AI-POD Configuration for Your Specific Workload?

Multi-rack AI fabric design • GPU-to-storage ratio optimization • Custom VRAM/memory sizing • Pre-installed AI frameworks • Network fabric topology • Rack layout and power planning • Direct engineering team collaboration

For CIOs & AI Infrastructure Architects: Quote-Ready AI Factory Building Blocks

Fixed reference configurations eliminate custom engineering for every AI project. Select your AI-POD or individual components, request a BoQ, and deploy — no GPU sizing guesswork, no fabric design from scratch.

Complete AI-POD Solutions

Pre-validated 1-rack solutions with compute, storage, fabric, and ops bundled into a single BoQ. GenAI inference, vision AI, and training pods — each with fixed BOM mapping.

Price-Locked Configurations

Every AI-GPU, AI-STG, and AI-FAB model has fixed specs and locked pricing. CIOs make price-only decisions — no architecture debates, no scope creep, no surprise line items.

On-Premises Data Sovereignty

All AI compute, training data, and model weights stay on-premises. Critical for regulated industries, government AI initiatives, and IP-sensitive organizations deploying private AI.

Composable Architecture

Start with a single AI-POD, scale to multi-rack with the 400GbE spine option. Every component is designed to compose — AI-GPU nodes, storage blocks, and fabric integrate without redesign.

Private AI Factory vs Cloud GPU — Total Cost of Ownership

For organizations running sustained AI workloads, on-premises AI-POD infrastructure delivers predictable costs, zero per-hour GPU billing, and complete data sovereignty at a fraction of long-term cloud GPU spend.

Zero Per-Hour GPU Billing

Fixed CapEx replaces variable per-hour cloud GPU charges. 24/7 GPU availability without billing anxiety — cost doesn't scale with utilization.

Complete AI Data Control

Training data, model weights, inference logs, and RAG corpora never leave your premises. Eliminates DPDPA compliance risk and IP exposure for private AI deployments.

Guaranteed GPU Availability

No cloud GPU shortages, no spot instance interruptions, no region capacity constraints. Your AI-POD GPUs are dedicated and always available.

Predictable AI Infrastructure Budget

One-time CapEx + known support costs vs unpredictable cloud GPU bills. Finance teams can plan AI infrastructure spend accurately across fiscal years.

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For SIs, VARs & AI Integrators: AI Infrastructure Channel Economics

AI-POD infrastructure represents the highest-value deals in enterprise IT. Fixed reference configurations simplify proposals while delivering premium margins across compute, storage, fabric, and ops.

Premium Deal Value

  • Highest deal value: GPU + storage + fabric + ops stacks
  • Multi-year services: deployment, managed monitoring, AMC
  • Deal registration for AI-POD infrastructure opportunities
  • Expansion revenue: single pod → multi-rack via AI-FAB-6403

AI Infrastructure Sales Enablement

  • Cloud-to-on-premises ROI calculators and TCO battle cards
  • AI-POD proposal templates (GenAI/Vision/Training)
  • GPU and VRAM sizing guides for common AI workloads
  • Partner portal with AI infrastructure marketing materials

Technical Support Infrastructure

  • Pre-sales workload assessment and GPU sizing support
  • PoC program with GPU benchmark suites for customer eval
  • Factory acceptance testing (AI-OPS-6501) included
  • Post-sales technical backup with AI-specific escalation paths

Win AI Infrastructure Deals with Make in India Credentials

Strengthen partner proposals with RDP's MeitY-recognized manufacturing and PLI 2.0 selection — critical differentiators for enterprise AI infrastructure procurement and government AI modernization projects.

Government AI Infrastructure

Make in India credentials are mandatory for government AI infrastructure projects. MeitY recognition and PLI 2.0 selection qualify for preferential procurement in AI modernization tenders.

Enterprise RFP Differentiation

PLI 2.0 selection provides objective validation for enterprise AI infrastructure proposals. Strengthens positioning against import-dependent GPU server alternatives.

Brand Trust & Track Record

14 years of manufacturing track record with ISO 9001 quality systems. Reduces perceived risk for enterprise teams evaluating domestic OEM AI infrastructure platforms.

Supply Chain Reliability

28,000 sq.ft local manufacturing ensures predictable delivery for AI infrastructure deployments. Reduces geopolitical GPU supply chain risk with domestic integration capabilities.

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Ready to Deploy Your Private AI Factory?

Request a complete AI-POD BoQ with GPU compute, storage, fabric, and ops — or join our channel partner program for premium AI infrastructure margins.

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