Technical Highlights
A Balanced Stack for All Inclusive AI
Every subsystem is selected to sustain the data movement and thermal load of large multi-GPU training and inference environments.
GPU COMPUTE
NVIDIA HGX B200
8-GPU Blackwell architecture
15× higher inference throughput vs HGX H100
Advanced Transformer Engine & 5th-Gen NVLink
Multi-trillion parameter model support
1:1 GPU-to-NIC ratio (8 × 400 Gb/s)
Up to 96 GPUs per rack density
NETWORK FABRIC
Scale-Out GPU Networking
NVIDIA Quantum-2 InfiniBand (NDR 400 Gb/s)
NVIDIA Spectrum-X Ethernet up to 800 GbE
3.2 Tb/s aggregate bandwidth per node
Leaf-spine/Clos topology with RoCEv2
Sub-microsecond latencies for MPI
Near-linear scaling for distributed AI
COOLING
DDC Hybrid-Cooled Cabinets
52U sealed enclosure with hybrid cooling
30-40% energy reduction vs air cooling
Supports up to 200 kW per cabinet
3,500× more efficient heat transfer
Zero thermal throttling at 100% load
Drop-in solution (no raised floor)
SOFTWARE
NVIDIA AI Enterprise
Pre-installed, licensed, and validated
Base Command Manager (BCM) for orchestration
RunAI for multi-tenant GPU scheduling
Ready-to-run AI templates included
Integrated monitoring and logging
3-5 year NVIDIA support included
STORAGE
IBM Storage Scale System 6000
Up to 1.44 PB usable flash storage in 4U
330 GB/s sequential bandwidth
13 million IOPS random performance
Multi-protocol support (NFS, S3, GPFS)
NVIDIA GPUDirect Storage enabled
No storage bottlenecks
DELIVERY
Integrated Deployment & Support
Turnkey integrated system
Fully tested and optimized
Day-one optimal performance
50+ years combined HPC expertise
Single-vendor accountability
Future-proof architecture
15×
Higher AI inference throughput vs. HGX H100
96
GPUs per rack target density
30–40%
Cooling-energy reduction with hybrid cooling
330
GB/s-class sequential storage bandwidth
AI Factory in a Box
AgilityFlexAI B200 HGX POD
Production-ready AI infrastructure — integrated as one system
AgilityFlexAI combines NVIDIA HGX B200 compute, high-speed networking, DDC hybrid-cooled cabinets, IBM Storage Scale System 6000, and NVIDIA enterprise software in a pre-engineered platform designed to reduce deployment complexity.
Compute, fabric, cooling, storage, and software are balanced together before the system reaches the data center.
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Key Advantages
Designed to Remove Integration Guesswork
The pod approach treats every major subsystem as part of one validated AI platform.
Done Right the First Time
ADS integrates and validates the complete platform before delivery, reducing deployment risk and tuning cycles.
No Bottlenecks by Design
Compute, storage, cooling, and network fabrics are sized together so one subsystem does not starve another.
Single Point of Accountability
One partner coordinates the integrated solution and support path across the core components.
Independent Scalability
Compute and storage can expand along different curves as models, data sets, and user demand change.
Best-of-Breed Integration
NVIDIA, IBM, and DDC technologies are combined around a coherent system architecture.
Lower Facility Burden
Hybrid cooling supports very high rack densities while reducing reliance on traditional room-level air cooling.
AI Infrastructure
Where the POD Fits
The architecture is aimed at organizations that need dense accelerated compute without taking on a multi-vendor integration project.
Large Model Training
Sustained distributed training across dense GPU nodes with high-speed east-west fabric.
High-Volume Inference
Scale-out inference for generative AI, recommendation, vision, and other accelerator-heavy services.
Research AI
Shared AI infrastructure for labs, universities, and engineering organizations with evolving model requirements.
Accelerated HPC
GPU-enabled simulation, scientific computing, and hybrid AI/HPC workflows.
Model Checkpointing
Parallel storage and high network bandwidth for frequent large checkpoint operations.
Multi-Team GPU Operations
Enterprise scheduling and orchestration for shared GPU capacity across teams and projects.
Applied Data Systems
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Stand Up an AI Factory
Want to Review the B200 POD Architecture?
ADS can map the pod to your model profile, facility constraints, storage requirements, and deployment timeline.
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