Top 5 Dedicated Server Providers with GPU [2026]

A dedicated GPU server offers something the cloud often struggles with: steady, predictable performance, controlled end to end. For large-model training, high-volume inference, and complex rendering, that consistency matters.

This article covers the top five dedicated GPU server providers. For each provider, we list the available GPU models, key features, their best applications, and provide a brief overview of the pros and cons.

#When to use a dedicated server with GPU

Choose a dedicated GPU server when control and consistency matter more than convenience. Here are some of the signs that it is the right choice.

• Workloads run continuously (training, high-volume inference, batch rendering).
• Multi-GPU scaling is required, ideally with NVLink or NVSwitch.
• Single-tenant isolation and root access are needed for drivers, kernels, and firmware.
• Data volumes are large, and egress fees in public clouds are significant.
• Predictable latency and steady throughput are essential.
• GPU partitioning is needed for many small concurrent jobs (MIG on supported GPUs).

#Top 5 dedicated server providers with GPU

If the goal is steady performance and full control, start here. These five providers offer modern GPUs, reliable networks, and transparent pricing, along with pros and cons to help you make an informed decision.

#Cherry Servers

Cherry Servers delivers bare-metal, dedicated GPU servers with modern NVIDIA accelerators. They prioritize control, performance, and transparent pricing. Provisioning for prebuilt nodes is fast, with custom builds available when needed.

They provide 24/7 human support, built-in DDoS protection, and a generous monthly transfer. Their data centers span Europe, North America, and the Asia Pacific. Typical use cases include AI/ML, HPC, and rendering. Cherry Servers are best suited for steady, compute-intensive work that requires single-tenant control, predictable costs, and straightforward automation.

#Key features

Here are some of Cherry Servers’ key features.

• Fast deployment Instant dedicated servers are ready in 15-30 minutes. Custom builds typically take 24-72 hours to complete. GPU servers are typically available within 24 hours.
• Modern GPU options Cherry Servers offers current NVIDIA accelerators tailored to different workload tiers. A100 and A40 are suitable for heavy training and large models. A10, A16, and A2 favor efficient inference and fine-tuning.
• Automation and low-level access Cherry Servers provides a web portal and REST API for handling repeatable provisioning, as well as IPMI/iKVM and custom OS installations for driver control and recovery.
• Generous transfer and DDoS protection Large included egress and always-on mitigation keep costs and downtime in check. This is useful for data-heavy pipelines and external inference endpoints.

#Pros

• Modern NVIDIA GPU options for training, inference, and rendering.
• Low entry price and clear, predictable billing.
• Local BGP and floating IPs for quick failover and traffic engineering.
• 24/7 human support with quick response from real engineers.
• On-demand IPMI/iKVM and custom ISO installs for full control and quick recovery.

#Cons

• Monthly public egress is capped at 100 TB per server. Any overage is billed per TB.
• GPU stock can fluctuate, and custom builds may take 24-72 hours.
• Smaller ecosystem and fewer managed services than large clouds.

#OVHcloud

OVHcloud provides dedicated GPU bare-metal servers in two lines: Scale-GPU (NVIDIA L4) and HGR-AI with NVIDIA L40S. These servers target AI training, high-throughput inference, and HPC, where steady performance and control are essential.

Networking is robust, featuring private links of up to 100 Gbps and a 99.99% server uptime SLA. Best fit for regulated EU workloads, traffic-heavy real-time services, and production environments that require strict availability.

#Key features

These are some of OVHcloud's features that stand out for production GPU work.

• Enterprise availability and resilience A 99.99% dedicated server SLA, dual power, and hardware redundancy ensure strict uptime targets are met.
• High-throughput private networking Private links reach 50–100 Gbps, with separate public bandwidth for steady external traffic.
• EU compliance and sovereignty EU-aligned certifications and data residency options help meet regulatory and public sector requirements.
• High-memory DDR5 with fast NVMe I/O Large RAM ceilings and NVMe SSD arrays sustain steady throughput for training data loads, feature stores, and checkpoints.

#Pros

• 99.99% SLA with dual power and hardware redundancy for production uptime.
• High-throughput private networking (50-100 Gbps) with separate 5-25 Gbps public bandwidth.
• vRack private interconnect with failover/BYOIP for multi-site clustering and quick reroutes.
• DDoS protection and dual power supplies for resilience.
• Free installation on dedicated GPU servers.

#Cons

• High entry price.
• Limited dedicated GPU choices (L4 or L40S). Other NVIDIA tiers are offered via Cloud GPU.
• Platform management feels complex for first-time users. The console and project setup take time to navigate.

#Datapacket

DataPacket provides dedicated NVIDIA GPU servers with unmetered bandwidth and low-latency global delivery. Plans cover modern GPUs from mainstream to high-end, including options suited for AI/ML, video transcoding, and real-time rendering.

Performance relies on a 270+ Tbps global backbone across 62 data centers, engineered for consistent latency and zero packet loss even under heavy traffic.

#Key features

These are the DataPacket features that stand out for globally distributed, bandwidth-heavy GPU work.

• Unmetered bandwidth on unshared uplinks Every server includes flat, predictable egress of up to 100-200 Gbps, with unshared uplinks (up to 2x100 GE) that are ideal for streaming, inference endpoints, and large data transfers.
• Global 270 Tbps+ backbone across 62 locations Optimized routing and broad geographic reach keep latency low and packet loss near zero, even for distributed or real-time workloads.
• Real-time support via a dedicated Slack channel Each customer gets direct Slack access to engineers and an account manager for 24/7 coordination on orders, incidents, and network changes.
• Automated post-install scripting A 2025 update introduced built-in scripts to handle post-deployment setup, including security hardening, package installation, and configuration, immediately after provisioning.

#Pros

• Wide GPU range from L4 to H100, with dual-GPU configurations for heavier loads.
• Same-day provisioning is available when hardware is in stock; most orders are completed within hours.
• Deep global peering with 16 transit providers and over 300 private interconnects enhances last-mile performance.
• Proactive routing and real-time monitoring ensure low latency and minimal packet loss.
• Two-tier DDoS protection, with Volumetric Shield included and optional Full Shield for stronger mitigation.

#Cons

• No managed platform services.
• Stock for top-tier GPUs varies by location. Some orders require a lead time, so please confirm availability before planning a launch.
• Final price scales with network options. 2x100 GE uplinks and Full Shield DDoS can materially increase the monthly cost.

#phoenixNAP

PhoenixNAP offers API-driven bare-metal GPU servers on its Bare Metal Cloud. The current GPU lineup centers on dual Intel Max 1100 cards per node, with 48 GB HBM2E on each GPU and Intel Xe Link for high-speed GPU-to-GPU communication.

You can deploy via API, CLI, or the web portal, and typical build times range from about 60 seconds to 15 minutes. GPU availability today is in Phoenix (AZ) and Ashburn (VA). PhoenixNAP best suits teams that prefer the Intel software stack (oneAPI, AMX) and require fast, automated provisioning on the U.S. coasts.

#Key features

Here are some of phoenixNAP’s key features.

• Dual Intel Max 1100 GPUs per node Two Max 1100 cards with 48 GB HBM2E each and Xe Link for high-bandwidth peer communication. No add-on licensing for GPU features.
• API-first control Full lifecycle via API and CLI, with Terraform and other IaC integrations for repeatable, code-driven provisioning.
• Intel software stack alignment Built to work with the Intel oneAPI toolchain and CPU-side accelerations such as AMX, which can speed up pre- and post-processing.
• Confidential-computing options SGX-capable CPU variants are available on selected builds to support enclave-based security models.

#Pros

• Modern HPE Gen12 hardware with Intel Xeon 6 across new builds.
• U.S. East and West GPU regions support bi-coastal redundancy and data residency.
• Reservation terms from 1 to 36 months can lower the total cost.
• Adjacent services such as colocation, backup, and DR are available under one provider.
• Including 15 TB egress on GPU nodes helps with cost planning.

#Cons

• Bare Metal Cloud GPUs are Intel-only.
• The per-node GPU count is limited to two, which restricts single-node training with 4-8 GPUs.
• GPU availability is limited to Phoenix and Ashburn.

#Hetzner

Hetzner offers budget-friendly dedicated GPU servers designed for reliable performance and straightforward management. Current GPU options include NVIDIA RTX 4000 SFF Ada and RTX 6000 Ada, paired with modern CPUs and NVMe storage. Traffic is unlimited on a 1 Gbit/s port, with an optional 10G uplink for higher throughput.

Data center parks are in Germany (Nuremberg, Falkenstein) and Finland (Helsinki), with 24/7 on-site service and ISO 27001-aligned infrastructure. Best for teams that want low entry cost and straightforward control in Europe-centric deployments.

#Key features

These are the Hetzner features that stand out for cost-efficient, EU-based GPU workloads.

• Affordable GPU tiers with flexible billing Entry GPU nodes start with the RTX 4000 SFF Ada. High-end models use the RTX 6000 Ada. Hourly and monthly billing are both available with no minimum term.
• Unlimited traffic at 1 Gbit/s Dedicated GPU servers include unlimited outgoing traffic on the default 1 Gbit/s port.
• 10G uplink option with simple overage A paid 10G addon includes 20 TB of outgoing traffic per month. Additional usage is billed at about €1 per TB.
• Operational tooling built in Robot/Rescue System, vSwitch (VLAN across locations), and basic DDoS protection simplify imaging, recovery, and private networking.

#Pros

• Low entry price for a dedicated GPU in Europe.
• Hourly billing and no minimum term for flexibility.
• Unlimited traffic on the standard 1 Gbit/s port.
• ISO-aligned, EU data center parks with 24/7 on-site staff.
• Useful built-ins like Rescue System, vSwitch, and basic DDoS protection.

#Cons

• The 10G uplink is a paid add-on with a 20TB monthly allowance. Extra egress is billed per TB.
• Hetzner only offers one GPU per server.
• Availability is limited to Hetzner data center parks in Nuremberg, Falkenstein, and Helsinki, resulting in optimal latency within Europe.

#Top picks by use case

• Choose Cherry Servers when you want a fast start and clear control for steady training or inference. It suits teams that need predictable performance, flexible billing, and straightforward ops across EU and U.S. locations.

• DataPacket is a good pick for bandwidth-heavy, globally distributed services. Their unmetered, dedicated uplinks and large backbone keep real-time APIs, streaming, and cross-region pipelines smooth under load.

• Choose OVHcloud for production estates that require strict uptime and EU-aligned compliance. The 99.99% SLA and private networking make sense when availability targets and sovereignty are top priorities.

• Select phoenixNAP if your stack leans into Intel. It fits pipelines that pair GPU work with oneAPI tools, AMX-accelerated CPU stages, or SGX needs, and you want quick, API-driven provisioning in U.S. regions.

• Choose Hetzner for cost-sensitive European deployments. It works well when you need reliable RTX Ada GPUs, simple operations, and unlimited traffic on a baseline port without complex add-ons.

#How to pick a dedicated GPU server

Start with the workload, not the hardware. Your goal is to match GPU class, network, and terms to the job and the budget.

1. Define the workload and model size Determine if it is training, inference, or rendering. Estimate VRAM from model, context length, and batch size. This decision guides everything else.
2. Match the GPU class to the VRAM first Pick a card that fits your memory. Then consider throughput and multi-GPU links. If it does not fit in VRAM, you will waste time on workarounds.
3. Choose a region with your data and users in mind Place compute near datasets and primary users to cut latency. Check data-residency rules and plan for failover if needed.
4. Run a representative test Prove performance and stability on the target setup before you scale. Benchmark the real model, batch, and precision you plan to use.
5. Understand the billing model Hourly, monthly, or reservation changes affect risk and total cost. Confirm how you are charged during idle time and what happens as usage grows.

#Conclusion

Dedicated GPU servers are well-suited for workloads where control, consistent performance, and predictable costs are essential. Each provider here fits a different need. Some advantages include increased bandwidth and global reach. Others focus on enterprise uptime or low entry price. Make your choice based on your workload, VRAM requirements, region, and budget.