Private AI Server
Architecture
From silicon to inference endpoint. We design, procure, and deploy GPU clusters, high-bandwidth networking, and production-hardened inference infrastructure—purpose-built for your AI workloads.
Architecture Layers
Seven layers engineered for maximum throughput, minimum latency, and absolute security.
Application Layer
API Gateway, Load Balancer, Rate Limiting, Authentication
Inference Layer
vLLM, TensorRT-LLM, Triton Inference Server, Model Router
Orchestration Layer
Kubernetes, Helm Charts, Terraform, GitOps Pipeline
Data Layer
Vector DB (Milvus/Qdrant), PostgreSQL, MinIO S3, Redis Cache
Compute Layer
NVIDIA H100/H200 GPUs, AMD MI300X, Intel Xeon CPUs
Network Layer
100GbE InfiniBand, RDMA, VXLAN Segmentation, BGP Routing
Physical Layer
Rack Design, Power Distribution, Liquid Cooling, Physical Security
GPU Cluster Configurations
Sized for your workload. Scalable as you grow.
| Config | GPUs | VRAM | Use Case | Models Supported |
|---|---|---|---|---|
| Inference Node | 2x H100 | 160 GB | Production inference | Up to 70B parameters |
| Training Cluster | 8x H100 | 640 GB | Fine-tuning & RAG | Up to 180B parameters |
| Sovereign Cluster | 16x H200 | 2.2 TB | Full training + multi-model | Up to 405B parameters |
| Edge Node | 2x L40S | 96 GB | Branch office inference | Up to 13B parameters |
High-Performance Networking
AI workloads are network-bound. We design for maximum throughput.
InfiniBand Fabric
400 Gb/s NDR InfiniBand for GPU-to-GPU communication. Sub-microsecond latency for distributed training workloads.
RDMA over Converged Ethernet
RoCEv2 for high-throughput, low-latency data movement between compute and storage nodes.
Zero-Trust Segmentation
VXLAN microsegmentation isolates AI workloads from corporate networks. Every flow authenticated and encrypted.
Multi-Site Connectivity
Encrypted site-to-site tunnels for distributed AI deployments. Automatic failover and load balancing.
GPU Direct Storage
Bypass CPU for direct GPU-to-storage data paths. Eliminates bottlenecks in data-intensive training pipelines.
Network Telemetry
Real-time bandwidth monitoring, congestion detection, and automated QoS adjustment for AI traffic prioritization.
Power & Cooling Engineering
AI clusters demand 10–40kW per rack. Standard cooling fails. We engineer for sustained compute.
Direct Liquid Cooling
Cold plate liquid cooling for GPU nodes delivering 100kW+ per rack. 40% more efficient than air cooling, enabling higher density deployments.
Redundant Power
2N+1 power architecture with UPS, generator backup, and automatic transfer switches. Zero downtime during power events.
Immersion Cooling
For maximum density deployments: single-phase immersion cooling eliminates fans, reduces PUE to 1.03, and extends hardware lifespan.
Energy Monitoring
Per-node power monitoring with real-time dashboards. Automated workload scheduling optimizes energy consumption during peak/off-peak periods.
Built for AI.
Not Retrofitted.
Every architecture decision optimized for AI workloads from the ground up. Let us design your infrastructure.