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Choose a location below to view GPU server configurations and availability at that data center.
Choosing where to deploy a dedicated GPU server in the USA comes down to one factor above all others: proximity to your end users or data sources. Network latency compounds with distance, and for workloads like real-time inference, live rendering, or interactive AI applications, that latency directly affects the experience on the other end.
U.S. data center demand generally clusters around three regions, each suited to a different traffic pattern:
East Coast locations sit closest to major internet exchange points serving the Northeast corridor and offer strong connectivity to European routes, making them a common choice for applications serving both U.S. and transatlantic users.
Central U.S. locations provide a balanced middle ground, useful when your user base is spread across the country rather than concentrated on either coast.
West Coast locations offer the shortest routes to Pacific traffic and are frequently paired with proximity to major tech hubs, which matters for teams that want low-latency access to their own infrastructure alongside their customer-facing deployments.
Beyond geography, match your GPU server location to your workload type. Training jobs that run for extended periods and don't serve live traffic are less sensitive to regional placement — what matters more there is compute availability and cost. Inference and rendering workloads that respond to real-time requests benefit far more from being deployed close to the traffic they serve.
The U.S. data center market is generally divided into three main regions: East, Central, and West. For optimal low-latency performance, it's best to deploy data centers in or near three main areas. Most U.S. data centers are located in or around the top metro markets, with significant demand currently seen in cities like Ashburn, Northern Virginia, Silicon Valley, and Northern California. New Jersey and New York are also strong contenders for data center deployments.
A dedicated GPU server is a physical machine with one or more GPUs allocated exclusively to a single customer no virtualization overhead, no resource contention from other tenants, and no unpredictable performance swings caused by "noisy neighbors" on a shared host. This is fundamentally different from a cloud GPU instance, where compute is often sliced across multiple customers or throttled during peak demand.
For workloads like large language model fine-tuning, computer vision training, or batch rendering, that distinction isn't academic it's the difference between a training job that finishes in a predictable window and one that doesn't. Dedicated hardware gives you full root access to configure drivers, CUDA versions, and kernel-level settings exactly the way your pipeline requires, without waiting on a shared platform's update schedule.
Bare-metal GPU hosting in the USA specifically adds two more advantages: proximity to major internet exchange points for lower latency to North American users, and access to abundant, competitively priced power and bandwidth relative to many international markets.
GPUYard provisions U.S.-based dedicated servers with NVIDIA hardware spanning entry-level rendering cards through data-center-grade accelerators, so you can match hardware to workload rather than overpaying for capacity you don't need.
| GPU | VRAM | Best For | Notes |
|---|---|---|---|
| NVIDIA H100 | 80GB | Large-scale LLM training, frontier model fine-tuning, highest-throughput inference | Top-tier accelerator, available as a configurable upgrade |
| NVIDIA A100 | 40GB / 80GB | Large-scale AI training, LLM fine-tuning, high-throughput inference | Data-center-grade Ampere architecture |
| NVIDIA L40S | 48GB | Inference, generative AI, graphics-heavy rendering pipelines | Strong middle ground between A100 and consumer GPUs |
| NVIDIA A40 | 48GB | Mixed graphics/compute, professional visualization, virtual workstations | Ampere architecture |
| NVIDIA L4 | 24GB | Inference, generative AI, video processing | Tensor Core-based, power-efficient |
| NVIDIA A10 | 24GB | Mixed graphics/compute, mid-tier inference | Balanced compute and graphics performance |
| GeForce RTX 5090 | 32GB | Latest-gen training experiments, high-end rendering, large local inference | Newest consumer-tier card |
| GeForce RTX 4090 | 24GB | Smaller ML experiments, rendering, dev/test workloads | Cost-effective; multi-GPU configs available at select locations |
U.S. data center placement minimizes round-trip latency for North American traffic and maintains strong connectivity to European routes, a meaningful factor for real-time inference and latency-sensitive applications.
Dedicated GPU servers are provisioned with high-throughput connections built to move large training datasets, model checkpoints, and rendered output without becoming a bottleneck.
Cisco firewalls and SSL are standard, with optional 250Gbps+ DDoS protection. Facilities run on N+1 redundant power configurations, backing a 100% uptime commitment.
Every dedicated GPU server is exclusively yours, full root access, no shared resources, and complete control over your software stack.
