PyTorch GPU Hosting | High-Performance Deep Learning Servers – B2BHostingClub

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PyTorch GPU Hosting Plans & Pricing

We offer cost-effective and optimized NVIDIA GPU rental servers for PyTorch with CUDA.

Advanced GPU Dedicated Server - V100

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  • 128GB RAM
  • GPU: Nvidia V100
  • Dual 12-Core E5-2690v3
  • 240GB SSD + 2TB SSD
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • Single GPU Specifications:
  • Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

Multi-GPU Dedicated Server - 3xV100

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  • 256GB RAM
  • GPU: 3 x Nvidia V100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Volta
  • CUDA Cores: 5,120
  • Tensor Cores: 640
  • GPU Memory: 16GB HBM2
  • FP32 Performance: 14 TFLOPS

Enterprise GPU Dedicated Server - RTX A6000

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  • 256GB RAM
  • GPU: Nvidia Quadro RTX A6000
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • 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 - RTX 4090

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  • 256GB RAM
  • GPU: GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Linux / Windows 10/11
  • 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

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  • 256GB RAM
  • GPU: GeForce RTX 5090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Blackwell 2.0
  • CUDA Cores: 21,760
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS
  • This is a pre-sale product. Delivery will be completed within 2–10 days after payment.

Enterprise GPU Dedicated Server - A100

/mo

add to cart
  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS

Multi-GPU Dedicated Server- 2xRTX 4090

/mo

add to cart
  • 256GB RAM
  • GPU: 2 x GeForce RTX 4090
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ada Lovelace
  • CUDA Cores: 16,384
  • Tensor Cores: 512
  • GPU Memory: 24 GB GDDR6X
  • FP32 Performance: 82.6 TFLOPS

Multi-GPU Dedicated Server- 2xRTX 5090

/mo

add to cart
  • 256GB RAM
  • GPU: 2 x GeForce RTX 5090
  • Dual E5-2699v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Blackwell 2.0
  • CUDA Cores: 21,760
  • Tensor Cores: 680
  • GPU Memory: 32 GB GDDR7
  • FP32 Performance: 109.7 TFLOPS
  • This is a pre-sale product. Delivery will be completed within 2–10 days after payment.

Enterprise GPU Dedicated Server - A100(80GB)

/mo

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  • 256GB RAM
  • GPU: Nvidia A100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 19.5 TFLOPS

Multi-GPU Dedicated Server - 4xA100

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  • 512GB RAM
  • GPU: 4 x Nvidia A100
  • Dual 22-Core E5-2699v4
  • 240GB SSD + 4TB NVMe + 16TB SATA
  • 1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Ampere
  • CUDA Cores: 6912
  • Tensor Cores: 432
  • GPU Memory: 40GB HBM2
  • FP32 Performance: 19.5 TFLOPS

Enterprise GPU Dedicated Server - H100

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  • 256GB RAM
  • GPU: Nvidia H100
  • Dual 18-Core E5-2697v4
  • 240GB SSD + 2TB NVMe + 8TB SATA
  • 100Mbps-1Gbps
  • OS: Windows / Linux
  • Single GPU Microarchitecture: Hopper
  • CUDA Cores: 14,592
  • Tensor Cores: 456
  • GPU Memory: 80GB HBM2e
  • FP32 Performance: 183TFLOPS

6 Key Benefits of PyTorch Lightning

Here are 6 Key Benefits of PyTorch Lightning, a popular high-level framework built on top of PyTorch.

Cleaner, More Modular Code

PyTorch Lightning separates engineering from research logic. You write less boilerplate code (e.g., for training loops, validation steps, logging), and focus more on the model itself.

Built-In GPU & Multi-GPU Training

Easily train on multiple GPUs, TPUs, or multiple nodes with just a few lines of code. No need to manually write distributed training logic (like DDP or Horovod setup).

Scalable from Laptop to Cluster

Lightning works on everything from your local machine to cloud GPU servers, Kubernetes, and high-performance clusters — with the same code.

Built-In Logging, Checkpointing, and Early Stopping

Integrated support for: TensorBoard, WandB, MLflow, Model checkpointing, and Early stopping and learning rate schedulers.

Standardized Training Loop

Lightning handles training, validation, testing, and prediction loops internally, ensuring consistent and reproducible results.

Plugin & Callback Ecosystem

Easily extend or customize behavior using plugins and callbacks: Mixed precision training (AMP), Gradient accumulation, Custom learning rate schedulers and Profilers and debuggers.

Frequently asked questions

PyTorch is an open-source deep learning framework developed by Facebook's AI Research lab. It is widely used in both academia and industry due to its ease of use, dynamic computation graph, and robust library for tensor computations. PyTorch facilitates building and training neural networks with its extensive support for machine learning and deep learning tasks.
CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It enables developers to leverage the parallel processing power of NVIDIA GPUs for computationally intensive tasks. CUDA provides the necessary tools and libraries to run complex calculations and algorithms significantly faster than on a CPU alone.
PyTorch CUDA refers to the integration of CUDA support within the PyTorch framework. This integration allows PyTorch to utilize the powerful parallel processing capabilities of NVIDIA GPUs, enabling faster and more efficient computation for deep learning tasks.
Yes, PyTorch is compatible with CUDA 11.x. The PyTorch development team regularly updates the framework to support the latest CUDA versions, ensuring compatibility with newer GPU architectures and performance improvements.
As of July 2024, the latest stable version of PyTorch is 2.3.1, which supports CUDA 11.8 and CUDA 12.1. This allows users to benefit from the latest enhancements in GPU performance and features.
TensorFlow offers better visualization, which allows developers to debug better and track the training process. PyTorch, however, provides only limited visualization.
PyTorch has long been the preferred deep-learning library for researchers, while TensorFlow is much more widely used in production. PyTorch's ease of use makes it convenient for fast, hacky solutions, and smaller-scale models.
PyTorch is an open-source machine learning library used for developing and training deep learning models based on neural networks. It is primarily developed by Facebook's AI research group.
If you're training a real-life project or doing some academic or industrial research, then for sure you need a GPU for fast computation. We provide multiple GPU server options for you running deep learning with PyTorch.
If you're just learning PyTorch and want to play around with its different functionalities, then PyTorch without GPU is fine and your CPU in enough for that.
Today, leading vendor NVIDIA offers the best GPUs for PyTorch deep learning in 2022. The models are the RTX 3090, RTX 3080, RTX 3070, RTX A6000, RTX A5000, RTX A4000, Tesla K80, and Tesla K40. We will offer more suitable GPUs for Pytorch in 2023.
Feel free to choose the best plan that has the right CPU, resources, and GPUs for PyTorch.
Our bare metal GPU servers for PyTorch will provide you with an improved application and data performance while maintaining high-level security. When there is no virtualization, there is no overhead for a hypervisor, so the performance benefits. Most virtual environments and cloud solutions come with security risks.
B2BHOSTINGCLUB GPU Servers for Pytorch are all bare metal servers, so we have best GPU dedicated server for AI.

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