Purpose-Built AI Hardware
Running large language models in production requires hardware designed for sustained inference workloads. Consumer GPUs throttle under continuous load. Enterprise GPU servers from NVIDIA, Dell, Supermicro, and Lenovo provide the thermal management, power delivery, ECC memory, and interconnect bandwidth that production AI demands. We specify the exact configuration for your workload so you buy what you need, not what a vendor upsells.
NVIDIA A100 80GB
The workhorse of enterprise AI inference. 80 GB HBM2e memory handles 70B parameter models at INT8. PCIe or SXM form factor with NVLink for multi-GPU tensor parallelism. Proven reliability across thousands of data center deployments.
NVIDIA H100 SXM
Next-generation Hopper architecture with 80 GB HBM3 memory and 3.35 TB/s bandwidth. Transformer Engine with FP8 support delivers 2-3x inference throughput over A100 for the same power envelope. NVLink 4.0 at 900 GB/s between GPUs.
NVLink Topology
Multi-GPU inference requires high-bandwidth interconnect between GPUs. NVLink provides 600-900 GB/s bidirectional bandwidth versus 64 GB/s for PCIe Gen 5. Critical for tensor parallelism across 2-8 GPUs serving large models.
Inference Engine Optimization
vLLM with PagedAttention for memory-efficient batching. TensorRT-LLM for NVIDIA-optimized kernels. Triton Inference Server for multi-model serving. Each engine has tradeoffs in throughput, latency, and flexibility.
GPU Server Deployment
Profile
Workload analysis and sizing
Specify
Hardware SKUs and configuration
Deploy
Rack, power, network, cooling
Optimize
Inference engine tuning
Profile
Workload analysis and sizing
Specify
Hardware SKUs and configuration
Deploy
Rack, power, network, cooling
Optimize
Inference engine tuning
GPU Server Architecture
Server Platforms
The GPU is the most visible component, but the server platform determines reliability, serviceability, and total cost of ownership. We recommend based on your data center standards and vendor relationships.
NVIDIA DGX H100. The reference platform with 8x H100 SXM GPUs, NVSwitch fabric, dual Sapphire Rapids CPUs, 2 TB system memory, and 30 TB NVMe storage. Fully integrated and validated by NVIDIA. Premium price but zero integration risk. Ideal when time-to-production matters more than cost optimization.
Dell PowerEdge XE9680. 8x H100 or A100 in a Dell chassis with iDRAC enterprise management. Better integration with Dell storage and networking ecosystems. Dell ProSupport provides next-business-day hardware replacement. More cost-effective than DGX with comparable GPU performance.
Supermicro GPU servers. Highest flexibility in configuration. SYS-421GE-TNRT supports 8x SXM GPUs with competitive pricing. IPMI management. Best for organizations with existing Supermicro infrastructure and in-house hardware expertise.
Data Center Requirements
A single 8-GPU server draws 10-12 kW of power and requires corresponding cooling capacity. Planning for power, cooling, and network connectivity before procurement prevents costly surprises.
Power. Dual redundant power supplies, 200-240V three-phase input. UPS with 15-minute runtime for graceful shutdown. Generator backup for production workloads. Plan for 15 kW per server rack position including networking and cooling overhead.
Cooling. Rear-door heat exchangers or in-row cooling units for 10+ kW per rack. Liquid cooling (direct-to-chip) for H100 SXM reduces cooling infrastructure cost and enables higher density deployments. Ambient temperature monitoring with automated throttling alerts.
Networking. 100 GbE or InfiniBand for multi-node deployments. 25 GbE minimum for single-server inference. Dedicated management network for IPMI/iDRAC. VLAN isolation between inference traffic and management traffic.
Who This Is For
On-premises GPU servers are for organizations with existing data center capacity that want complete physical control over their AI infrastructure. The capital investment pays back within 12-18 months at moderate inference volumes compared to cloud GPU pricing.
Contact us at ben@oakenai.tech