GPU Servers for Machine Learning
Power up your machine learning models with Hostrunway’s high-performance GPU servers, optimized for AI-driven computing, large-scale data analysis, and deep learning applications.
- Powerful GPUs – NVIDIA A100, H100, RTX 4090 & AMD Instinct
- ML/AI Ready – Supports TensorFlow, PyTorch, and major frameworks
- Fast Network – Low-latency, high-bandwidth up to 10Gbps
- Custom Configs – Flexible CPU, RAM, GPU & storage options
- Global Locations – Data centers in UA, EU, Asia, Africa & Oceania
Power Your ML Projects with High-Speed GPUs
Accelerate every stage of your machine learning pipeline—from data preprocessing to model training and inference—with our dedicated GPU servers. Equipped with enterprise-grade NVIDIA and AMD GPUs, our infrastructure is optimized to handle large datasets, complex algorithms, and iterative experimentation efficiently. Build, train, and scale ML models faster with reliable, high-performance compute power tailored for real-world applications.
- Faster Training – Accelerate model training with high-performance GPUs
- Big Data Ready – Process large datasets with optimized throughput
- Framework Compatible – Supports TensorFlow, PyTorch, Scikit-learn & more
- AI-Tuned Servers – Built for all ML training types
- Global Deployment – Launch servers near your users or data
- Flexible & Affordable – Scale resources without overspending
Deploy Smarter, Pay Less
Harness the power of GPU servers engineered to accelerate machine learning workflows. Train advanced models rapidly, process large-scale datasets with ease, and scale your infrastructure dynamically—all while leveraging enterprise-grade GPUs optimized for diverse ML tasks. Achieve cutting-edge performance and cost-efficiency to drive your AI innovations forward.
Get a Flat 15% Discount
Subscribe for 12 months and get a flat 15% discount on your total billing amount.
| Processor | Memory | Storage | GPU | Data Transfer | Data Center | Price | |
|---|---|---|---|---|---|---|---|
| 1x E5-2670 2.50GHz 10C/20T | 32 GB | 500 GB | 1 x NVIDIA GeForce GTX 1060 | 30 TB | Miami |
$458/mo |
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| 1x E5-2650 2.20GHz 12C/24T | 32 GB | 500 GB SSD | 1 x NVIDIA GeForce GTX 1060 | 30 TB | Miami |
$498/mo |
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| 1x E5-2650 2.20GHz 12C/24T | 32 GB | 500 GB SSD | 1 x NVIDIA GeForce GTX 1070 | 30 TB | Miami |
$558/mo |
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| Core i3-9350KF 4.0GHz (4 cores) | 64 GB | 512Gb NVMe SSD | GTX 1080Ti+ASMB9-IKVM | 10Tb free (1Gbps) | Netherlands |
$378/mo |
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| Intel Xeon E-2288G 3.7GHz (8 cores) | 32 GB | 480Gb NVMe SSD | 1 × RTX A4000 | 10Tb free (1Gbps) | Netherlands |
$480/mo |
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| Intel Xeon E3-1284L Quad Core 1.80 GHz | 8 GB | SATA-SSD 480 GB | Intel Iris Pro 5200 | 100 Mbps Unmetered | New York |
$115/mo |
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| Intel Xeon E3-1284L Quad Core 2.90 GHz | 8 GB | SATA-SSD 240 GB | Intel Iris Pro P6300 | 100 Mbps Unmetered | New York |
$160/mo |
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| Intel Xeon AMD Ryzen 9 5900X 3.7GHz (12 cores) | 32 GB | 500Gb NVMe SSD | RTX 3080+PSU 700W | 10Tb free (1Gbps) | Russia |
$450/mo |
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| Intel Xeon AMD Ryzen 9 3900X 3.8GHz (12 cores) | 32 GB | 2x512GB NVMe SSD | RTX A4000 | 10Tb free (1Gbps) | Russia |
$490/mo |
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| Intel Xeon 8core | 32 GB | 250GB SSD | NVIDIA Tesla K80 24GB*1 | 10 Mbps | South Korea |
$448/mo |
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| Intel Xeon 16core | 64 GB | 480GB SSD or 1TB SATA X2 | NVIDIA Tesla K80 24GB*2 | 10 Mbps | South Korea |
$988/mo |
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| Intel Xeon 32core | 128 GB | 1TB SSD | NVIDIA Tesla K80 24GB*4 | 10 Mbps | South Korea |
$179/mo |
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| Intel Xeon 64core | 256GB | 2TB SSD | NVIDIA Tesla K80 24GB*8 | 10 Mbps | South Korea |
$358/mo |
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| Intel Xeon 2 x E5-2650 2.20GHz 24C/48T | 64 GB | 1TB SSD | NVIDIA Tesla P100 | 30 TB | Miami |
$139/mo |
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| Intel Xeon 2 x E5-2650 2.20GHz 24C/48T | 128 GB | 2TB SSD | 2 x NVIDIA Tesla P100 | 30 TB | Miami |
$279/mo |
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| Intel Xeon 2 x E5-2620 2.10GHz 12C/24T | 32 GB | 250GB SSD | 3 x NVIDIA GRID K520 | 30 TB | Miami |
$998/mo |
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| Intel Xeon 2 x E5-2620 2.10GHz 12C/24T | 32 GB | 250GB SSD | 3 x NVIDIA Tesla K10 | 30 TB | Miami |
$998/mo |
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Dedicated GPU Server
Made possible by our multiple datacenter locations, redundant cooling, emergency generators. Monotonectally drive business e-markets after distinctive functionalities.
View PricingCloud GPU Server
Our experts are on standby for friendly, pro-level support 24/7, 365. No question is too Objectively envisioneer stand-alone growth strategies whereas market.
How GPUs Power Modern Machine Learning
GPU servers for machine learning leverage massively parallel architectures with thousands of CUDA or stream processors to accelerate tensor and matrix operations central to deep neural networks. They combine high-bandwidth HBM or GDDR VRAM, PCIe Gen 4/5 or NVLink interconnects, and optimized libraries such as cuDNN, CUDA, TensorRT or ROCm to deliver low-latency, high-throughput compute for training CNNs, RNNs, transformers, and LLMs at scale. This architecture minimizes memory bottlenecks and enables larger batch sizes, mixed-precision (FP16/BF16) training, and rapid checkpointing for complex models.
In production ML pipelines, GPUs are used across the lifecycle: feature extraction, model training, hyperparameter optimization, and real-time inference for latency-sensitive APIs. Multi-GPU and multi-node setups with data-parallel or model-parallel strategies (e.g., PyTorch DDP, ZeRO, tensor and pipeline parallelism) allow horizontal scaling of workloads while maintaining high GPU utilization. Combined with fast NVMe storage, high-bandwidth networking, and orchestration via containers or Kubernetes, GPU infrastructure forms the backbone of modern AI and deep learning platforms.
- United States
- Miami, USA
- New York, USA
- Dallas, USA
- Denver, USA
- Kansas City, USA
Specs Not Listed? Let’s Build It!
Every business has unique requirements. If our listed configurations don’t match what you’re looking for, we’ll design a dedicated server that fits your exact specs. No compromises, just the right solution - built around your workload.
Engineered for Performance
Dedicated GPU Servers deliver fast, scalable, and efficient computing with NVIDIA & AMD GPUs, PCIe Gen 4 SSDs, and AI-optimized performance for AI, ML, HPC, and rendering.
High-Performance GPU
Powered by NVIDIA A100, H100, RTX 4090, and AMD Instinct for AI, ML, and HPC.
Parallel Processing
Accelerates deep learning, rendering, and big data analytics with CUDA & OpenCL support.
Tensor & CUDA Core Optimization
Leverages NVIDIA CUDA, TensorRT, and ROCm for AI, ML, and deep learning tasks.
Dedicated Resources
Full control over compute power with no resource sharing for maximum efficiency.
FPGA & Hybrid Compute Support
Combine GPUs, CPUs, and FPGAs for specialized high-performance tasks.
Optimized for AI & HPC
Ideal for AI model training, simulations, financial modeling, and scientific research.
Multi-GPU Support
Deploy multiple NVIDIA & AMD GPUs in a single server for parallel processing power.
Scalable Configuration
Custom CPU, RAM, storage, and bandwidth options to meet diverse workload needs.
High-Performance GPUs
- Multi-GPU setup for parallel processing
- CUDA & OpenCL for AI & ML
- Supports NVIDIA A100, H100, RTX 4090, AMD
- Dedicated GPUs for max compute power
- Optimized tensor cores for deep learning & HPC
GPU Passthrough & Virtualization
- Supports GPU passthrough for VMs & containers
- SR-IOV support for virtualized GPU acceleration
- Direct GPU access for HPC
- AI-driven workload optimization
- Dynamic resource scaling based on demand
Ultra-Fast Storage Options
- PCIe Gen 4 NVMe for fast access
- RAID configurations for data redundancy
- Cloud storage integration for hybrid workloads
- Hybrid HDD + SSD for performance
- HBM for AI & ML acceleration
Compute & Memory Optimization
- Intel Xeon & AMD EPYC, high cores
- Up to 4TB DDR5 ECC RAM
- Configurable CPU-GPU pairing
- Overclocking support for performance tuning
- Multi-threaded performance for AI model training
Energy Efficiency & Cooling
- Liquid cooling options for high-performance GPUs
- Smart fan control for temperature regulation
- Eco-friendly, low-carbon data centers
- Redundant power supply & UPS backup
- Advanced heat dissipation for performance
Global Deployment & Scalability
- Data centers across USA, Europe, Oceania, & Asia
- Low-latency with global servers
- Instant deployment & rapid provisioning
- Multi-region availability for global AI workloads
- Seamless migration options for AI & HPC applications
Choose the Right GPU for Your Needs
Hostrunway provides NVIDIA A100, H100, RTX 4090, and AMD Instinct GPUs for AI, ML, and rendering with low-latency and scalable solutions globally.
Gaming, 3D Rendering, Simulations & VR-Ready GPUs
Gaming GPUs are built for high frame rates and detailed graphics, ensuring smooth gameplay at 4K and supporting VR, ray tracing, and DLSS for an immersive experience.
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NVIDIA GeForce RTX 4090
Offers unmatched performance for 4K gaming and supports real-time ray tracing.
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AMD Radeon RX 7900 XTX
A powerful option for high-resolution gaming, offering excellent value for performance.
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NVIDIA RTX 3080
Perfect for video editing, 3D rendering, and apps like Premiere Pro and Blender.
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AMD Radeon RX 6800 XT
A powerful GPU offering top performance in video editing, rendering, and creative software support.
Whether you're into competitive gaming or immersive open-world experiences, a gaming GPU will ensure you get the most out of your games.
AI, Machine Learning & Mining GPUs
AI and ML require GPUs with parallel processing power, specialized cores (e.g., Tensor cores), and high VRAM to accelerate neural network training and data analysis.
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NVIDIA A100 Tensor Core GPU
Optimized for data centers and AI, delivering top performance for deep learning.
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AMD Radeon Pro VII
A strong choice for ML tasks, offering great value for high-performance computing and scientific research.
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NVIDIA GeForce RTX 3080 Ti
Known for high hash rates in crypto mining with efficient power consumption.
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AMD Radeon RX 5700 XT
A popular choice for mining, balancing performance and energy efficiency.
If you're looking to speed up training and inference times in AI, choosing a GPU built for parallel processing will significantly enhance your productivity.
FAQs about GPU Servers for Machine Learning
Machine learning engineers, data scientists, and AI teams often have similar questions when choosing and configuring GPU servers for their workloads, from selecting the right GPU model to optimizing performance and costs. This FAQ section addresses the most common technical and implementation queries so you can quickly validate compatibility, understand resource requirements, and plan a scalable ML infrastructure on GPU-powered servers.
What is a GPU server for machine learning?
A GPU server is a high-performance compute server equipped with one or more graphics processing units (GPUs) in addition to standard CPUs, designed to accelerate parallel workloads like deep learning and scientific computing.
Do I really need a GPU for machine learning?
For deep learning workloads such as CNNs, RNNs, transformers, and large language models, a dedicated GPU is considered essential because CPU-only training is often too slow and inefficient at scale.
What ML workloads benefit most from GPU servers?
Workloads like image and video recognition, NLP, recommendation systems, fraud detection, simulation, and LLM/Generative AI training and fine-tuning gain the largest speedups from GPU acceleration.
How do I choose the right GPU for my ML project?
High-end GPUs such as NVIDIA H100, A100, and L40S are ideal for large models, multi-GPU training, and enterprise AI pipelines, while mid-range GPUs may be sufficient for smaller models or experimentation.
Can multiple GPUs be used together?
Yes. Multi-GPU setups with NVLink or PCIe scaling allow distributed training across GPUs, improving throughput and handling larger datasets.
Can I deploy ML APIs and microservices on a GPU server?
Yes, you can deploy REST or gRPC inference APIs in containers or VMs on a GPU server, using load balancers and CI/CD pipelines to manage rolling updates and horizontal scaling.
What drivers and libraries are required to use GPUs for ML?
A typical stack includes a compatible NVIDIA driver, CUDA toolkit, cuDNN, and optionally TensorRT for optimized inference, matched carefully to the versions of your chosen ML framework.
Which ML frameworks are supported on GPU servers?
Popular frameworks such as TensorFlow, PyTorch, JAX, and MXNet support GPU acceleration via CUDA, cuDNN, and other NVIDIA libraries to speed up both training and inference workloads.
Can GPU servers be used for both training and inference?
Yes. Training requires maximum GPU compute and VRAM, while inference benefits from optimized GPU cores for real-time predictions.
How do GPU servers reduce training time?
A model that takes weeks on CPUs can often be trained in days or hours on GPUs due to parallelized matrix multiplications.
Should I choose cloud or dedicated GPU server for ML?
Cloud GPUs offer flexibility and on-demand scaling, while dedicated or bare-metal GPU servers provide more consistent performance, lower latency, and better cost-efficiency for sustained or high-intensity workloads.
How can I maximize GPU utilization on my ML server?
Using efficient data pipelines, larger batch sizes (within VRAM limits), mixed precision training, and multi-GPU parallelism helps keep utilization high and reduce idle GPU time.
What Customer Say About Us
At Hostrunway, we measure success by the success of our clients. From fast provisioning to dependable uptime and round-the-clock support, businesses worldwide trust us. Here’s what they say.
James Miller
USA – CTOHostrunway has delivered an exceptional hosting experience. The server speed is consistently high and uptime is solid. Highly recommended!
Ahmed Al-Sayed
UAE – Head of InfrastructureOutstanding reliability, fast response times, and secure servers. Onboarding was smooth and support is amazing.
Carlos Ramirez
Mexico – CEOLightning-fast servers and great support team. Secure, stable, and enterprise-ready hosting.
Sofia Rossi
Italy – Product ManagerStrong hosting partner! Fast, secure servers and real-time assistance from their tech team.
Linda Zhang
Singapore – Operations DirectorExcellent performance, great scalability, and proactive support. Perfect for enterprises.
Oliver Schmidt
Germany – System ArchitectPowerful servers, flawless uptime, and top-tier support. Great value for enterprise hosting.
Trusted for Mission-Critical Workloads
Whether you’re launching your first application or operating large-scale global infrastructure, Hostrunway delivers complete hosting solutions to support every stage of growth. From dedicated servers and cloud hosting to GPU servers and high-performance workloads, we provide enterprise-grade performance with the flexibility and speed modern businesses need—backed by real experts, not automated scripts.
