Custom AI Servers for Training, Inference & Enterprise AI
Multi-GPU servers engineered for production AI workloads. From dual RTX 5090 builds to 8-way H100 clusters, we design hardware matched to your model architecture, throughput targets, and compliance requirements.
Training Servers vs. Inference Servers
Two fundamentally different hardware strategies for two different workload profiles.
Training Servers
- Maximum aggregate VRAM: 288GB+ with 3x RTX PRO 6000 Blackwell or 8x H100 SXM5
- NVLink/NVSwitch interconnects at up to 900 GB/s per link for distributed training
- 512GB to 2TB ECC DDR5 for ZeRO-3 CPU offloading
- InfiniBand or RoCE networking for multi-node gradient synchronization
Inference Servers
- Optimized for low latency and high throughput with vLLM continuous batching
- RTX 5090 at 1,792 GB/s memory bandwidth for maximum tokens-per-second
- PagedAttention and KV-cache optimization for concurrent request handling
- Load-balanced API endpoints with automatic failover
Server Configurations We Build
Every server is purpose-built for your workload. We run these same configurations in our own datacenter.
Multi-GPU Inference Servers
2 to 4 RTX 5090 GPUs for high-throughput production inference. Serves quantized models up to 30B parameters per GPU at production latency targets.
Large Model Training Rigs
3x RTX PRO 6000 delivers 288GB total VRAM for fine-tuning models up to 70B parameters. The same configuration powering our ptg-rtx production server.
Datacenter Training Clusters
4 to 8-way H100 configurations with NVSwitch fabric for all-to-all GPU communication. Built for training models from scratch at scale.
Compact Inference Nodes
NVIDIA DGX Spark with Grace Blackwell Superchip. Runs quantized models up to 200B parameters in a desktop form factor under 500W.
RAG Pipeline Servers
Mixed GPU allocation for embedding generation, vector search, and LLM completion. Optimized for the full retrieval-augmented generation stack.
AMD GPU Servers
Largest single-GPU VRAM pool available. Production-viable alternative for organizations seeking vendor diversification with ROCm 6.x support.
Custom Build vs. Off-the-Shelf
Thermal Throttling Under Load
OEM servers optimize for acoustics, not sustained AI workloads. Performance drops after hours of continuous GPU utilization.
Locked Firmware and Limited GPUs
Vendor-locked BIOS, restricted GPU options, and proprietary cooling limit your hardware choices and upgrade paths.
Weeks of Environment Setup
Servers arrive with basic driver installs. Your team spends weeks debugging CUDA compatibility and framework conflicts.
Sustained Peak Performance
Cooling engineered for 24/7 GPU utilization. Same throughput at hour 72 of a training run as minute one.
Full Hardware Control
Unrestricted BIOS access, any GPU from RTX 5090 to H200, and upgrade paths that never void warranties.
Production-Ready on Delivery
72-hour burn-in tested. Pre-configured with PyTorch, CUDA, vLLM, and your full AI stack validated end-to-end.
How We Build Your Server
Requirements analysis and architecture design
Component sourcing and procurement
Assembly, security hardening, and OS configuration
72-hour burn-in under sustained AI workloads
AI software stack installation and validation
Delivery, deployment, and ongoing support
Built For
Frequently Asked Questions
How much does a custom AI server cost?
Configurations range from $15,000 for a dual-GPU inference server to $250,000+ for 8-way H100 training clusters. We provide detailed cost comparisons against equivalent cloud GPU spend over 12, 24, and 36 months so you can evaluate the investment.
What GPUs do you recommend for LLM training?
For models up to 30B parameters, the RTX PRO 6000 Blackwell (96GB) handles single-GPU fine-tuning. For 70B+ models, multi-GPU configurations with 288GB+ aggregate VRAM using RTX PRO 6000 or H100 are required. We analyze your specific model architecture to determine the optimal GPU selection.
Can your servers meet CMMC and HIPAA requirements?
How long does a custom server build take?
Typical builds take 2 to 4 weeks from design approval to delivery, depending on component availability. Rush builds with in-stock components can ship in 7 to 10 business days. GPU availability for datacenter-class cards like H100 may extend timelines.
Do you provide hosting for servers you build?
Yes. We offer managed GPU server hosting from our datacenter with redundant power, enterprise cooling, and 24/7 monitoring. You can also deploy on-premise with our remote management support.
What software comes pre-installed?
Servers ship with your complete AI stack validated: CUDA or ROCm, PyTorch, TensorFlow, vLLM, TensorRT, container runtimes, and monitoring tools. The full environment is tested under load before delivery so you avoid weeks of compatibility troubleshooting.
Explore More AI Hardware Solutions
Ready to Build Your AI Server?
Get a custom architecture proposal with performance projections and cloud cost comparison included.