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PyTorch is a fully featured framework for building deep learning models.
PyTorch GPU Hosting - Single and Multi-GPU Training Solutions
Rent a virtual (VPS) or a dedicated server with pre-installed PyTorch - a free and open-source machine learning library. Simply choose the right plan, configure a server and start working in just 15 minutes.
PyTorch is provided only for leased HOSTKEY servers. To get a PyTorch license, select it in the "Panels Software" tab while ordering the server.
Rent a reliable VPS in the Netherlands, Finland, Germany, Iceland, Turkey and the USA.
Server delivery ETA: ≈15 minutes.
Rent a dedicated server with a full out-of-band management in the Netherlands, Finland, Germany, Turkey and the USA.
Server delivery ETA: ≈15 minutes.
PyTorch is a free and open-source deep learning framework built to be flexible and modular for research, with the stability and support needed for production deployment. It is released under the modified BSD license.
We guarantee that our servers are running safe and original software.
To install PyTorch, you need to select a license while ordering a server on the HOSTKEY website. Our auto-deployment system will install the software on your server.
If you have any difficulties or questions when installing and/or using the software, carefully learn the documentation on the official website of the developer, read about typical problems and how to solve them or contact PyTorch support.
A PyTorch server gives the resources needed to develop, train and apply machine learning models built on PyTorch. They are best used for processing big datasets, building sophisticated neural networks and running real-time analysis. If ML teams move demanding tasks to a dedicated PyTorch server, they can work more quickly, ensure their results are the same and rely on stable performance in production.
All HOSTKEY PyTorch servers are built to be scalable. You can enhance important parts such as RAM, CPU or GPU whenever you need, without transferring your workloads. This makes it easy for teams to deploy on a small scale and then add more resources as their work develops, without any service interruptions.
No problem. HOSTKEY offers many pre-configured environments that include well-known machine learning libraries such as TensorFlow, Scikit-learn, JAX and Hugging Face Transformers. You may use these environments together with PyTorch or join several libraries in one project to fulfill your needs.
Yes. All PyTorch server rentals from HOSTKEY give you access to our expert support team at any time. If you need help with your environment, optimizing training, fixing GPU driver problems or configuring Docker, our experts are ready to help at any time to ensure you continue working productively.
Absolutely. HOSTKEY is known for offering custom services designed for tough machine learning tasks. If your project requires a custom mix of hardware, including more GPU memory, high IOPS storage or better CPUs, we can design a server that suits you. Researchers, startups and businesses looking for unique AI applications will find custom specs very useful.
To run a PyTorch model on a GPU you need to transfer both the model and the tensors to a CUDA device:
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Documentation 
Login device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
x = x.to(device)
output = model(x)This makes sure computations are sent to the GPU.
You can view the device of a tensor or model:
print(next(model.parameters()).device)Or check tensor placement:
print(x.device)If it shows cuda:0 (or another GPU index), PyTorch is using your GPU.
Run:
import torch
print(torch.cuda.is_available())If it returns True, CUDA is available and PyTorch can use the GPU.
No, PyTorch does not automatically use GPUs. You must explicitly move your model and tensors to the GPU using .to("cuda") or .cuda().
You can use torch.nn.DataParallel or torch.nn.parallel.DistributedDataParallel:
model = torch.nn.DataParallel(model)
model = model.to("cuda")For large-scale training, DistributedDataParallel is recommended for better performance and scalability.
device = torch.device("mps")Yes, PyTorch provides functions to manage GPU memory:
print(torch.cuda.memory_allocated())
print(torch.cuda.memory_reserved())torch.cuda.empty_cache()PyTorch is a machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.
Create, train, and scale models on infrastructure that's purpose-built for PyTorch. Our platform is designed for low latency, high throughput and fast iteration - from first prototype to production at scale.
GPUs are crucial to PyTorch because they speed up tensor math, convolution, attention, and backpropagation by orders of magnitude compared to CPUs. This means shorter training cycles, faster A/B experiments and better utilization of your engineering time.
We support the latest Nvidia GPUs (including RTX 4090/5090, RTX 6000 PRO, RTX A5000, Tesla A100, H100) and AMD accelerators optimized for ROCm. Choose bare metal servers for maximum, predictable performance or flexible cloud-based server instances that spin up in minutes.
Use cases include research and fine-tuning to large-scale inference. Whether you want one card for quick experiments, or you want to have multiple cards for distributed training, we have the right building blocks and control for you.
Get CUDA + cuDNN aligned for your GPU generation for peak performance. We provide drivers, test GPU visibility and ship a verified PyTorch build so you can push code immediately.
If you're migrating or automating CI/CD, our images help you standardize across teams so your pytorch install gpu steps are repeatable and version-located.
AMD accelerators are a good alternative with competitive FP16/BF16 throughput and a growing ecosystem support.
Skip manual dependency management Choose an image with:
From pytorch gpu install to production, images reduce drift and save time on every new environment.
GPU acceleration squeezes the training time. Expect 10-100X speedups depending on model family & batch size. Iterate faster, more experiments per day, better validation scores sooner.
For spiky workloads choose hourly and for reserved capacity and best rates choose monthly. Switch between the two as your project increases or your budget changes.
Access top‑tier NVIDIA GPUs (RTX 4090/5090, A5000, RTX 6000 PRO, Tesla A100, H100) and AMD MI‑series for cost‑effective throughput under ROCm. Mix and match per project.
GPU experts help you with details of the kernel/driver, DDP, NCCL/RCCL, mixed precision, and data pipeline optimization to keep your cluster productive.
From single-GPU runs to multi-node distributed training, choose the specific accelerators you need - consumer-class for budget experiments, pro cards for high VRAM or data center GPUs for maximum reliability.
Provision in minutes with automation that validates drivers, runtime and interconnects. Your environment is ready when you are.
Run near your users or data. Reduce latency, meet data residency requirements, and increase throughput with regional choices of EU, US and beyond.
Isolated tenancy, hardened hypervisors, dedicated firewalls, and monitored uptime SLAs make your training runs safe and predictable.
Pro tip: quick validation for pytorch use gpu is torch.cuda.is_available() and torch.cuda.device_count() right after login.
GPU hosting is managed compute for training and inference using PyTorch. You bring code and data, we take care of drivers, accelerators, and throughput so that your experiments are stable and reproducible.
Train SOTA architectures using efficient batch sizes, gradient checkpointing, BF16/TF32. Quickly experiment with different schedulers, optimizers, and augmentations.
Transition from notebook to service is fast. Validate PMF, iterate on user feedback, and control burn with hourly billing
Fine-tune domain models, execute secure inference endpoints, and control SLAs. Tie into MLOps for versioning, rollbacks, and auditability.
Hands-on labs and teaching clusters that remain the same semester to semester. Enable students to learn distributed training without wrestling with drivers.
For Nvidia, CUDA gives them the parallel compute runtime and cuDNN gives them deep neural network primitives. Matching PyTorch builds to GPU generation (Hopper, Ampere, Ada) for optimal kernels and memory planners.
ROCm is AMD's open software stack to enable GPU compute with HIP, MIOpen, and RCCL. We provide images and guidance to map CUDA style workflows to ROCm where appropriate - perfect for pytorch amd gpu teams.
DistributedDataParallel (DDP) is the standard for scaling training across multiple GPUs or nodes. We validate NCCL/RCCL, networking MTU and topology, so your gradients sync efficiently with little idle time.
Where available, NVLink is a high-bandwidth GPU-to-GPU link that reduces inter-GPU communication overhead. This opens up bigger batch sizes and smoother scaling for transformer and vision models.
The higher the VRAM, the larger the context windows, the longer the sequences, and the fewer gradient accumulation steps. Choose according to your model footprint and expected batch size.
Cloud is flexible; dedicated is fixed capacity and isolation for compliance or steady utilization. Many customers start in the cloud and migrate steady workloads to reserved nodes.
Hourly is great for spikes and weekend experiments. Monthly reservations provide the best value for your money: euro per TFLOP for continuous training.
Right-size your batch based on GPU: balance utilization and convergence. With DDP, keep global batch as per_gpu_batch * world_size. Profile dataloaders to avoid CPU bottlenecks that starve your GPUs - this is especially important for pytorch multiple gpu and multi gpu pytorch projects.
Leverage AMP/BF16 for more throughput and memory headroom. Validate loss scaling and pay attention to numerics in attention and normalization layers. Mixed precision can often provide 1.5 - 2.5x speedups.
Standardize training loops, checkpointing and logging. Lightning allows it to switch between single and multi gpu strategies easily - for example, when you pytorch with multiple gpu during scale up. It also eliminates boilerplate to allow you to concentrate on experiments, not plumbing.
Port/Traffic: always 1 Gbps.
Availability varies by region and may change; contact sales for a tailored quote.
Bare‑Metal GPU Servers (Pre‑installed PyTorch)
VPS with Dedicated GPU (Pre‑installed PyTorch)
All plans come with a validated PyTorch stack and ready for DDP and mixed precision. They are ideal for pytorch cloud gpu solutions where you want to get from prototype to production quickly.
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Deploy Now — Launch your first instance in minutes and start training on day one.
