Dedicated GPU servers configured specifically for your AI framework and workload type. We deploy NVIDIA H100 and H200 for large-scale transformer training, A100 for mixed-precision workloads and production inference, L40S for inference-heavy and rendering applications, and RTX 4090 or RTX 5090 for cost-efficient development, fine-tuning, and smaller model hosting.

What sets us apart: We do not just rent you a GPU. We tune kernel parameters, configure CUDA Toolkit and cuDNN for your specific framework version, optimize NCCL for multi-GPU communication, benchmark your particular model architecture, and deliver a system ready to execute your training scripts on day one. You get root access, custom OS images, and zero noisy-neighbor problems because the hardware is fully dedicated to your workloads.

Our GPU hosting includes redundant power delivery, liquid cooling for high-density configurations, 100 Gbps networking, and around-the-clock monitoring with automatic failover protocols. Every server is stress-tested under full GPU load for 72 or more hours before we hand over access.

Typical use cases: LLM fine-tuning and pre-training, diffusion model training, real-time inference APIs, computer vision pipelines, reinforcement learning environments, and multi-modal model development. Related: AI model fine-tuning services.

Off-the-shelf servers are not designed for GPU-accelerated artificial intelligence. Memory bandwidth bottlenecks, inadequate PCIe lane allocation, and insufficient power delivery limit performance and reliability under sustained workloads. We design custom server builds that eliminate these constraints from the start, giving your models the throughput they need.

Our build process: We profile your model's vRAM requirements, I/O patterns, and parallelization strategy. Then we architect a system with the right CPU platform such as AMD EPYC or Intel Xeon, appropriate RAM capacity ranging from 512 GB to 2 TB or more, NVMe storage arrays in parallel RAID configurations for maximum throughput, and redundant power supply units rated for continuous full-load GPU draw over extended periods.

For multi-GPU builds, we configure NVLink bridges for direct GPU-to-GPU communication that bypasses PCIe bottlenecks and set up high-speed networking for distributed training across nodes. Every build undergoes stress testing under full GPU load for at least 72 hours before deployment to verify thermal stability and performance consistency under real-world conditions.

Example configurations: Eight-GPU RTX 5090 workstation for local development and prototyping, four-GPU A100 80 GB training server with NVLink, eight-GPU H100 SXM cluster with high-speed fabric interconnect, and hybrid inference clusters using L40S GPUs for balanced throughput and cost efficiency.

Production inference demands fundamentally different infrastructure than training. Lower latency, horizontal scaling, model versioning, and cost efficiency matter more than raw training throughput. We deploy managed inference clusters using TensorRT optimization, ONNX Runtime, vLLM, or Triton Inference Server depending on your model architecture and latency requirements.

What we handle: Quantization and pruning to reduce model size without meaningful accuracy loss, batch inference optimization for maximum GPU utilization, auto-scaling based on real-time request volume, A/B testing between model versions, continuous monitoring of latency, throughput, and GPU utilization metrics, and cost allocation across inference endpoints so you understand exactly where your compute budget goes.

We deploy inference workloads on RTX GPUs for cost-efficient FP16 and INT8 models, L40S for balanced performance across diverse workloads, or A100 and H100 for high-throughput serving of large models. Load balancing distributes requests intelligently across GPU pools, and caching layers reduce redundant inference calls for frequently requested outputs.

Ideal for: API-based model serving, real-time recommendation engines, chatbot and AI agent backends, image generation services, embedding generation at scale, and any application requiring sub-100 millisecond inference latency. See also our custom AI development services.

If you own GPU hardware but lack the facility to run it properly for production workloads, our colocation service provides enterprise-grade hosting in Raleigh, NC. We offer rack space with redundant power, cooling infrastructure engineered for high-density GPU racks, fiber connectivity with multiple upstream providers, physical security controls, and remote hands support whenever you need it.

Included with colocation: Multiple upstream internet providers with BGP routing for redundancy, 10 Gbps to 100 Gbps connectivity options, IPMI and BMC remote management access, biometric-controlled physical access, around-the-clock monitoring, and SLA-backed uptime guarantees. Unlike consumer ISPs, we provide static IP blocks, reverse DNS, and no bandwidth throttling regardless of usage volume.

For organizations with data sovereignty and compliance requirements, we offer air-gapped colocation with no internet connectivity, physically isolated network segments, and encrypted-at-rest storage. This configuration meets CMMC, ITAR, HIPAA, and other compliance frameworks requiring strict data isolation and access controls.

Managed hosting adds: Operating system patching, driver updates, CUDA version management, storage expansion planning, proactive hardware replacement, and continuous monitoring. You focus on AI development and model improvement while we handle every aspect of infrastructure operations. Learn more about our AI compliance capabilities.

Large language models, diffusion models, and video generation models in 2026 require multi-GPU or multi-node training infrastructure to achieve practical training timelines. A single-GPU training run that takes weeks can complete in days with proper parallelization across a well-architected cluster. We deploy distributed training clusters with NVLink for intra-node GPU communication and high-speed fabric for inter-node communication.

Cluster architecture: Multiple training nodes, each equipped with four to eight GPUs, interconnected via 200 Gbps or faster switching fabric. Shared parallel file systems such as Lustre or BeeGFS provide high-throughput dataset access across all nodes simultaneously. Job scheduling through Slurm or Kubernetes manages multi-user access and resource allocation efficiently, preventing contention between teams or workloads.

We configure NCCL, DeepSpeed, or FSDP for efficient gradient synchronization, implement gradient checkpointing to reduce vRAM usage on memory-constrained configurations, and optimize data loading pipelines to eliminate GPU idle time that wastes expensive compute cycles. Every cluster includes monitoring dashboards showing per-GPU utilization, memory usage, thermal status, and training throughput metrics updated in real time.

Ideal for: Pre-training foundation models on proprietary corpora, fine-tuning LLMs on domain-specific datasets, hyperparameter sweeps across large search spaces, ablation studies, and any training workload that benefits from scaling beyond eight GPUs.

Not every workload belongs in the cloud, and not every workload belongs on-premises. The right answer depends on your usage patterns, budget constraints, security requirements, and scaling needs. We help organizations design hybrid GPU architectures that optimize across all four dimensions simultaneously.

On-premises GPU infrastructure is best for continuous training workloads, sensitive or regulated data, and scenarios with predictable resource needs. ROI breakeven typically occurs within six to twelve months of heavy utilization. You benefit from fixed costs, zero egress fees, and full hardware control over every aspect of the stack.

Cloud GPU instances are best for bursty workloads, rapid experimentation, and scenarios requiring instant scaling to dozens or hundreds of GPUs. Pay-per-use pricing works well for intermittent jobs but becomes prohibitively expensive for workloads running around the clock.

Hybrid approach: Run core training on dedicated infrastructure and burst to cloud during peak demand periods. Deploy inference on cost-efficient local GPUs while training on high-performance clusters. Preprocess data on CPU-optimized systems and send prepared datasets to GPU clusters for model training.

We model total cost of ownership across every deployment option, factor in your workload patterns and growth projections, and recommend the architecture that maximizes return on investment while meeting your security and compliance requirements. Explore our AI implementation consulting for strategic planning support.