GPU Infrastructure & Resource Architecture
Explore how GPU Mart designs, deploys, and manages high-performance GPU infrastructure. This overview explains our GPU allocation model, hardware selection strategy, storage architecture, and performance isolation principles that power reliable AI and compute workloads.
Get StartedGPU Product Line and Selection Strategy
GPU Mart's GPU portfolio is designed and deployed based on application-specific requirements, architectural generations, long-term maintainability, and compute efficiency. We prioritize modern GPU architectures and mainstream models, and continuously update our offerings in line with NVIDIA's technology roadmap.
All GPU resources are centrally procured, deployed, and managed by the Database Mart infrastructure team, and are governed under a unified lifecycle management framework, forming a standardized GPU infrastructure pool.
| Tier | Primary Use Cases | Key Characteristics | Representative Models |
|---|---|---|---|
| Entry-Level GPU | Development, testing, lightweight AI inference, remote graphics | Low power consumption, high stability, suitable for entry-level compute workloads | GTX 1650, GTX 1660 |
| Mainstream Performance | Model inference, computer vision, rendering, general-purpose computing | Strong performance-to-cost ratio, mature software ecosystem, broad AI framework compatibility | RTX 2060 / 3060 Ti / 4060 / 5060 Series |
| Workstation-Class GPU | Professional computing, enterprise AI development, stability-critical workloads | Professional-grade drivers, larger memory capacity, extended support lifecycles | RTX A-Series, RTX Pro Series |
| Data Center GPU | Large-scale model training, high-performance computing (HPC) | Optimized for training and HPC workloads, deployed exclusively in data center environments | A100, H100 |
GPU Allocation Model
GPU Mart assigns GPU resources using PCIe GPU passthrough, providing each GPU VPS and dedicated GPU server instance with a full physical GPU. Resources are provisioned with a one-to-one hardware mapping, with no partitioning, time-sharing, or oversubscription.
PCIe GPU Passthrough
Each GPU VPS and GPU Dedicated Server instance receives a full physical GPU via PCIe passthrough — no shared GPU slicing, no vGPU partitioning, no time-sharing between tenants. This one-to-one hardware mapping ensures predictable, bare-metal-level performance and full CUDA compatibility for every workload.
GPU Virtualization & Performance Consistency
GPU VPS services are built on KVM virtualization combined with hardware-level GPU passthrough. This architecture delivers near-native GPU performance while maintaining virtualization flexibility and workload isolation — enabling customers to deploy AI and compute workloads with consistent and stable performance across every instance.
Multi-GPU & NVLink Interconnect
For distributed training and parallel compute workloads, GPU Mart supports multi-GPU dedicated server deployments with optional NVLink high-speed interconnect. These configurations are designed for large model training, multi-GPU inference, and high-performance computing workloads that exceed single-GPU capacity.
Centralized Infrastructure Management
All GPU assets are centrally procured and lifecycle-managed through the DBM infrastructure system. GPU resources are deployed under dedicated allocation policies with no oversubscription, supported by continuous infrastructure monitoring, tenant isolation controls, and baseline security and backup mechanisms.
Platform & Software Support
GPU Mart provides a standardized compute platform designed for AI, high-performance computing, and GPU-accelerated workloads, built around virtualization efficiency, hardware-level GPU allocation, and optimized runtime environments.
Optional preconfigured runtime stacks reduce deployment complexity for AI and content generation workloads.
GPU Mart supports containerized GPU workloads and cluster-based compute environments across VPS and dedicated GPU infrastructure.
Customers maintain full control over operating systems, applications, AI frameworks, and runtime configurations. GPU Mart provides optional preconfigured environments but does not restrict customer-managed stacks.
Storage Architecture
GPU Mart's storage architecture is designed to minimize GPU idle time caused by I/O bottlenecks, supporting high-throughput AI training, dataset loading, and parallel compute pipelines.
GPU Mart deploys NVMe SSD storage as high-performance workload storage across GPU VPS and most dedicated GPU server deployments. NVMe storage is the baseline infrastructure standard for modern AI and GPU-accelerated workloads.
For dedicated GPU servers, RAID storage configurations may be deployed depending on workload and customer requirements. RAID design is selected based on performance, redundancy, and capacity balancing requirements.
Operations, Security & Performance
GPU Mart maintains standardized operational controls across performance optimization, security isolation, and infrastructure reliability — ensuring consistent, predictable compute for every tenant.
Virtio drivers for storage and networking reduce latency and improve throughput across the virtualization layer.
Upgradeable network bandwidth up to 1 Gbps with dual-stack public networking (IPv4 and IPv6) support.
Network-level tenant isolation, baseline threat mitigation, anti-mining protection, and hardware-level GPU allocation prevent cross-tenant interference.
Automated full-disk backups with retention cycles automatically scheduled based on the selected plan size — typically 2-week or 4-week intervals.
Frequently Asked Questions
How does GPU virtualization work?
Are GPU resources shared between customers?
How is workload performance isolation ensured?
Do you resell or broker third-party GPU capacity?
What types of workloads is your infrastructure designed for?
Can customers customize software environments and runtime configurations?
Ready to Deploy on GPU Mart Infrastructure?
From GPU VPS with dedicated PCIe GPU passthrough to multi-GPU server configurations, GPU Mart's infrastructure is built for reliable, high-performance AI and compute workloads.