AI-POD System SKUs
Complete 1-rack AI Factory solutions with fixed BOM mapping — GenAI Inference, Vision AI, and Training pods
Model No. 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 Storage | Core)
Fabric:
1× AI-FAB-6401 (100GbE Lossless Fabric HA Pair)
Ops:
AI-OPS-6501 + 6502 recommended
Best for
Private copilots, RAG, embeddings, enterprise inference services (CIOs, GCCs, BFSI, manufacturing HQ)
Model No. 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 Storage | Entry)
Optional Hot:
+ AI-STG-6301A (recommended for faster indexing)
Fabric:
1× AI-FAB-6401 (100GbE Lossless Fabric HA Pair)
Best for
Video analytics, factory safety, quality inspection, surveillance intelligence (manufacturing plants, warehouses, campuses)
Model No. 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 Storage | Core)
Dataset Lake:
+ AI-STG-6302A/6302B (recommended)
Fabric:
1× AI-FAB-6402 (200GbE Lossless Fabric 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 decision for CIOs
A) 4-GPU Inference Entry (48GB VRAM class)
Model No. 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
Model No. 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)
Model No. AI-GPU-72201
4-GPU Inference Node (Core | Intel)
Chassis:
4U GPU server, redundant fans, redundant 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
Model No. AI-GPU-72202
4-GPU Inference Node (Core | AMD)
Chassis:
4U GPU server, redundant fans, redundant 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)
Model No. 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
Model No. 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)
Model No. 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
Model No. 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 retention tiers for AI workloads
1) AI Hot Tier (Vector DB, Hot corp, Checkpoints, Active datasets)
Model No. 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
Model No. 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 + replay)
Model No. AI-STG-6302A
Capacity + Throughput Storage (Entry | 4U 60-Bay | HA Controllers)
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
Model No. 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 tier 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 options
Model No. AI-FAB-6401
100GbE Lossless Fabric (HA Pair | Pod Scale)
Design:
2× Top-of-Rack switches (HA pair), MLAG/vPC
Ports:
32×100GbE QSFP28 per switch
Features:
Lossless Ethernet (PFC/ECN), LACP/MLAG, QoS templates
Telemetry:
Port health, link flaps, error counters
Cabling:
DAC/optics for up to 4× AI-GPU nodes + 1× AI-STG hot storage
Management:
1× mgmt port per switch, RBAC, audit logs
Best for
Stable throughput between GPU nodes + hot storage for inference & vision pods — default fabric for AI-POD-7101 and AI-POD-7102
Model No. 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 traffic
Telemetry:
Fabric health baselines
Cabling:
For up to 2× 8-GPU training nodes + 1× hot NVMe storage
Management:
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
Model No. 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 and wants leaf-spine fabric
Best for
Multi-rack scale (3–10 racks), no redesign later — optional for future expansion
AI-OPS (Ops/Acceptance Options)
Deployment readiness and lifecycle management for AI infrastructure
Model No. 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, error counters, redundancy failover
Inventory:
Serials, MACs, firmware versions captured
Outputs:
Acceptance report + baseline performance sheet + handover pack
Best for
"Go-live confidence" with validation and sign-off — applies to every AI-POD, recommended for standalone AI-GPU
Model No. AI-OPS-6502
Day-2 Ops Runbook Pack (Recommended)
Monitoring:
GPU health, temps, fan, power, ECC errors, NVMe wear, fabric errors
Alerts:
Thresholds + escalation matrix
Baselines:
Driver/firmware baseline (known-good versions)
Patch Cadence:
Quarterly recommended + rollback method
Spares:
PSUs, fans, NVMe, optics recommendation
RMA:
Workflow + response SLAs (as per warranty tier)
Outputs:
Runbook PDF + monitoring checklist + escalation contacts
Best for
Ensure customer can run AI factory without chaos — recommended for all AI-PODs
Model No. AI-OPS-6503
Managed Monitoring + Patch Cadence (Optional)
Includes:
Monthly health dashboard (GPU, storage, fabric)
Patch Window:
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
Model No. 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