High-Performance GPU Linux Server with NVIDIA CUDA
Rent a fully dedicated linux nvidia server optimized for AI training, deep learning, and GPU-accelerated computing. Every linux gpu server supports CUDA, cuDNN, PyTorch, TensorFlow, Ollama, and more — on your choice of Linux distribution.
Everything your linux nvidia cuda workload demands
Every gpu linux plan comes with dedicated NVIDIA hardware, high-speed networking, and enterprise security — built to run the most demanding AI and HPC workloads on Linux.
Dedicated NVIDIA GPU — 100% Yours
Every linux gpu server comes with a fully dedicated NVIDIA card — no vGPU slicing, no shared VRAM. You get all CUDA cores, full memory bandwidth, and consistent throughput every second of your rental.
Full CUDA Support on Every Linux GPU Server
Every linux nvidia cuda environment is compatible with the full NVIDIA software stack. CUDA, cuDNN, PyTorch, TensorFlow, Ollama, and DeepSeek are all supported — select models come pre-installed with Ollama, DeepSeek, Llama, and GPT-OSS.
High-Speed, Low-Latency Networking
Every linux gpu server includes high-throughput, unmetered bandwidth with dedicated IPs, hardware firewall, and free DDoS mitigation — ensuring real-time inference, large dataset transfers, and distributed training never hit a bottleneck.
Enterprise-Grade Security
Cisco firewall, strict access control, and continuous monitoring protect your data on every gpu linux instance. Isolated environments ensure full resource exclusivity.
24/7/365 GPU Expert Support
NVIDIA Linux specialists available around the clock for setup, CUDA driver configuration, performance tuning, and framework troubleshooting. Most issues resolved in under one hour.
Built for every gpu linux workload
From research to production, our Linux GPU servers handle the most demanding AI and HPC tasks with ease.
AI Training & Deep Learning
Accelerate model training on a dedicated linux nvidia cuda environment. Optimized for CNNs, Transformers, GANs, and large language model fine-tuning using PyTorch and TensorFlow on Linux.
Real-Time Model Inference
Deploy trained models on linux gpu server infrastructure for low-latency, high-throughput inference. Power chatbots, image recognition APIs, and recommendation engines at scale.
3D Rendering & Visualization
Run Blender, Unreal Engine, and NVIDIA OptiX on multi-GPU nvidia linux servers. RTX 4090 setups deliver real-time ray tracing and accelerated rendering pipelines.
HPC & Scientific Computing
GPU-parallel processing on gpu linux crushes climate modeling, molecular dynamics, and financial risk simulations — workloads that would take weeks on CPU clusters.
Stable Diffusion & Image Generation
Run AUTOMATIC1111, ComfyUI, and InvokeAI on ubuntu nvidia or fedora nvidia servers. High VRAM GPUs generate full-resolution images at maximum batch speed.
Scalable AI SaaS & Model Hosting
Linux GPU Servers support high-concurrency AI SaaS and model API services. They allow flexible resource scaling and stable operation, providing reliable and fast AI computing for startups and enterprise platforms.
Choose the Right Linux NVIDIA GPU for Your Workload
Match your use case to the recommended GPU configuration for optimal performance on your linux gpu server.
| Workload | Recommended GPUs | Why It Works |
|---|---|---|
| AI & Machine Learning Training | H100, A100, RTX Pro 6000, RTX A6000 | These GPUs feature massive memory, dense Tensor Cores, and high FP32/BF16 compute power, enabling efficient training of large-scale models and distributed AI workloads. |
| Real-Time AI Inference / Low-Latency Services | RTX 5090, RTX 4090, RTX A4000 | With high CUDA and Tensor Core counts combined with fast memory, these GPUs deliver rapid model inference and consistent low-latency performance for real-time applications. |
| Scientific Computing & Data Analysis | V100, A100, H100 | Their high-precision compute capabilities and large memory allow efficient execution of large-scale matrix operations, scientific simulations, and complex data analytics. |
| 3D Rendering & Video Processing | RTX 4090, RTX 5090, RTX Pro 6000, RTX A6000 | The combination of many CUDA cores, Tensor Cores, and large memory enables fast rendering of complex scenes, high-resolution video processing, and AI-accelerated denoising. |
| Accelerated App & Algorithm Development | RTX 3060 Ti, RTX 4060, RTX 5060 | These GPUs offer moderate memory and solid compute performance, providing cost-effective acceleration for development, prototyping, and algorithm testing. |
| Scalable AI SaaS & Model Hosting | A100, H100, RTX Pro 5000, RTX A5000 | Large memory, strong compute power, and multi-instance virtualization allow these GPUs to reliably host multiple models and support multi-tenant AI services. |
Your Favorite Distro, Nvidia-Ready
Whether you prefer ubuntu nvidia for its broad ecosystem, fedora nvidia for cutting-edge kernel support, or enterprise-grade CentOS and AlmaLinux — all six distributions fully support NVIDIA drivers and CUDA, enabling seamless deployment of your GPU workloads.
Top-tier linux gpu server performance at honest prices
All plans include dedicated NVIDIA GPU, unmetered bandwidth, and 24/7 expert support. No hidden fees.
Advanced GPU VPS- RTX Pro 5000
- 60GB RAM
- 24 CPU Cores
- 320GB SSD
- 500Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 5000
- CUDA Cores: 14,080
- Tensor Cores: 440
- GPU Memory: 48GB GDDR7
- FP32 Performance: 66.94 TFLOPS
Enterprise GPU VPS- RTX Pro 6000
- 90GB RAM
- 32 CPU Cores
- 400GB SSD
- 1000Mbps Unmetered Bandwidth
- Once per 2 Weeks Backup
- OS: Windows / Linux
- Dedicated GPU: Nvidia RTX Pro 6000
- CUDA Cores: 24,064
- Tensor Cores: 852
- GPU Memory: 96GB GDDR7
- FP32 Performance: 126 TFLOPS
Basic GPU Dedicated Server - RTX 4060
- 64GB RAM
- GPU: Nvidia GeForce RTX 4060
- Eight-Core E5-2690
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 3072
- Tensor Cores: 96
- GPU Memory: 8GB GDDR6
- FP32 Performance: 15.11 TFLOPS
Advanced GPU Dedicated Server - RTX 3060 Ti
- 128GB RAM
- GPU: GeForce RTX 3060 Ti
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Basic GPU Dedicated Server - RTX 5060
- 64GB RAM
- GPU: Nvidia GeForce RTX 5060
- 24-Core Platinum 8160
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Blackwell 2.0
- CUDA Cores: 4608
- Tensor Cores: 144
- GPU Memory: 8GB GDDR7
- FP32 Performance: 23.22 TFLOPS
Advanced GPU Dedicated Server - A4000
- 128GB RAM
- GPU: Nvidia Quadro RTX A4000
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Advanced GPU Dedicated Server - V100
- 128GB RAM
- GPU: Nvidia V100
- Dual 12-Core E5-2690v3
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
Enterprise GPU Dedicated Server - RTX A6000
- 256GB RAM
- GPU: Nvidia Quadro RTX A6000
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Enterprise GPU Dedicated Server - A100
- 256GB RAM
- GPU: Nvidia A100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Enterprise GPU Dedicated Server - RTX 4090
- 256GB RAM
- GPU: GeForce RTX 4090
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Ada Lovelace
- CUDA Cores: 16,384
- Tensor Cores: 512
- GPU Memory: 24 GB GDDR6X
- FP32 Performance: 82.6 TFLOPS
Enterprise GPU Dedicated Server - RTX 5090
- 256GB RAM
- GPU: GeForce RTX 5090
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Blackwell 2.0
- CUDA Cores: 21,760
- Tensor Cores: 680
- GPU Memory: 32 GB GDDR7
- FP32 Performance: 109.7 TFLOPS
Enterprise GPU Dedicated Server - H100
- 256GB RAM
- GPU: Nvidia H100
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- Single GPU Specifications:
- Microarchitecture: Hopper
- CUDA Cores: 14,592
- Tensor Cores: 456
- GPU Memory: 80GB HBM2e
- FP32 Performance: 183TFLOPS
CUDA
A parallel computing platform by NVIDIA that enables GPUs to accelerate AI training, scientific computing, and high-performance applications.
Docker
A containerization platform that allows you to package applications and dependencies for consistent deployment across environments.
Jupyter
An interactive web-based environment for writing code, running experiments, and visualizing data in real time.
Trusted by AI teams & developers worldwide
GPU-Mart's linux nvidia cuda setup was flawless. We went from deployment to training in under 20 minutes. The dedicated RTX 4090 handles our LLM fine-tuning with zero throttling.
Switched from a cloud provider to GPU-Mart's linux gpu server for Blender rendering. The fedora nvidia environment was perfectly configured. Multi-RTX setup cut our render pipeline by 60% — and support answered in under an hour.
Running climate simulations on ubuntu nvidia servers — the performance is outstanding. Full root access, 1 Gbps networking, and nvidia-smi just works. GPU-Mart is the only provider I trust for serious HPC on Linux.
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Everything about GPU Linux servers
Common questions about our linux nvidia infrastructure, CUDA configuration, and distro support.
Browse All PlansWhich Linux distributions are supported on your GPU servers?
Is CUDA pre-installed on Linux GPU servers?
Are the GPU resources on Linux VPS shared with other users?
How do I access my Linux GPU server?
Can I use Docker with NVIDIA GPU on Linux?
Is there a free trial available for Linux GPU servers?
Deploy your Linux NVIDIA GPU Server today
Join AI engineers, researchers, and developers running mission-critical workloads on dedicated linux nvidia cuda infrastructure at GPU-Mart.