Dedicated Nvidia GPU
When you rent a GPU server, whether it's a GPU dedicated server or GPU VPS, you benefit from dedicated GPU resources. This means you have exclusive access to the entire GPU card.
Hosted AI and Deep Learning Dedicated Server
GPUs can offer significant speedups over CPUs when it comes to training deep neural networks. We provide bare metal servers with GPUs that are specifically designed for deep learning and AI purposes.
Plans & Prices of GPU Servers for Deep Learning and AI
We offer cost-effective NVIDIA GPU optimized servers for Deep Learning and AI.
Why Choose our AI Server Hosting
Database Mart enables powerful GPU hosting features on raw bare metal hardware, served on-demand. No more inefficiency, noisy neighbors, or complex pricing calculators.
Premium Hardware
Our GPU dedicated servers and VPS are equipped with high-quality NVIDIA graphics cards, efficient Intel CPUs, pure SSD storage, and renowned memory brands such as Samsung and Hynix.
Full Root/Admin Access
With full root/admin access, you will be able to take full control of your dedicated GPU servers for deep learning very easily and quickly.
99.9% Uptime Guarantee
With enterprise-class data centers and infrastructure, we provide a 99.9% uptime guarantee for hosted GPUs for deep learning and networks.
Dedicated IP
One of the premium features is the dedicated IP address. Even the cheapest GPU dedicated hosting plan is fully packed with dedicated IPv4 & IPv6 Internet protocols.
24/7/365 Free Expert Support
Our dedicated support team is comprised of experienced professionals. From initial deployment to ongoing maintenance and troubleshooting, we're here to provide the assistance you need, whenever you need it, without extra fee.
How to Choose the Best GPU Servers for AI and Deep Learning
Here's a concise comparison table summarizing the key performance metrics of NVIDIA GPUs that matter most for deep learning and AI workloads:
Key NVIDIA GPU Performance Metrics
| Metric | Description | Why It Matters | Recommended Use Cases |
|---|---|---|---|
| VRAM (Memory Size) | Amount of GPU memory (e.g., 24GB, 80GB) | Determines max model size, batch size, input resolution | Training large models, high-res data, LLMs |
| Memory Bandwidth | Speed of memory access (GB/s) | Affects data throughput between GPU cores and memory | Large datasets, 3D/vision models |
| CUDA Cores | Parallel processing units | Impacts raw compute performance for FP32 | General training and simulation |
| TFLOPS (FP16/FP32/INT8/FP8) | Trillions of operations per second | Direct measure of compute power (lower precision = faster) | FP16/BF16 for training, INT8/FP8 for inference |
| Tensor Cores | Specialized matrix multiplication cores | Accelerates deep learning (GEMM ops) using low-precision formats | CNNs, transformers, LLMs |
| NVLink / PCIe Bandwidth | GPU-to-GPU communication speed | Crucial for multi-GPU performance and distributed training | LLM training, large model parallelism |
| Power Consumption (TDP) | Energy draw under load (e.g., 400W–700W) | Impacts server power/cooling requirements | Important for hardware planning and cost |
| Software/Driver Support | Compatibility with CUDA/cuDNN/NCCL, etc. | Ensures the GPU is usable with your DL framework | Always verify for latest PyTorch/TensorFlow |
⚠️ Tips:
- Prioritize VRAM and Tensor Core performance for training.
- For inference at scale, focus on INT8/FP8 TFLOPS, low power usage, and memory efficiency.
- For multi-GPU setups, NVLink support and NCCL compatibility are critical.
🧠 Top NVIDIA GPUs for PyTorch & TensorFlow – Comparison Table (2025)
| GPU | Arch | VRAM | FP16 TFLOPS | FP8 Support | Tensor Cores | Best Use Case | Notes |
|---|---|---|---|---|---|---|---|
| H100 | Hopper | 80GB HBM3 | ~200+ | ✅ | 4th-gen | Cutting-edge training (LLMs, multi-GPU) | Fastest GPU, supports FP8, NVLink/NVSwitch |
| A100 (80GB) | Ampere | 80GB HBM2e | ~78 | ❌ | 3rd-gen | Large models, multi-GPU training | Most-used datacenter GPU |
| A100 (40GB) | Ampere | 40GB HBM2 | ~78 | ❌ | 3rd-gen | Multi-GPU training, research | Half memory of 80GB version |
| RTX 5090 | Blackwell | ~32–48GB | ~160+ (est) | ✅ | 5th-gen | Single-GPU high-end training | Consumer-grade successor to 4090 |
| RTX 4090 | Ada Lovelace | 24GB GDDR6X | ~83 | ❌ | 4th-gen | R&D, vision/NLP training | Best performance-per-dollar single-GPU |
| RTX A6000 | Ampere | 48GB GDDR6 ECC | ~39 | ❌ | 3rd-gen | Large model training, research | ECC VRAM, workstation-grade |
| RTX A5000 | Ampere | 24GB GDDR6 ECC | ~27 | ❌ | 3rd-gen | Vision/NLP training | Workstation-friendly, mid-tier pro GPU |
| RTX A4000 | Ampere | 16GB GDDR6 ECC | ~20 | ❌ | 3rd-gen | Light training, inference | Low power, compact form factor |
| V100 | Volta | 16GB or 32GB HBM2 | ~15.7 | ❌ | 2nd-gen | Legacy model training | Still relevant, but aging |
Freedom to Create a Personalized Deep Learning Environment
The following popular frameworks and tools are system-compatible, so please choose the appropriate version to install. We are happy to help.
🧠 Common Open-Source AI & Deep Learning Frameworks
| Framework | Language | Primary Use | Key Features | Best For |
|---|---|---|---|---|
| PyTorch | Python, C++ | Research, training, inference | Dynamic computation graph, intuitive debugging, active community | Researchers, startups, CV/NLP developers |
| TensorFlow | Python, C++ | Training, deployment, cross-platform | Static & dynamic graphs, strong deployment tools (TF Lite, TF Serving) | Enterprises, production environments |
| JAX | Python | Mathematical modeling, research, performance | High-performance autodiff, NumPy-like syntax, great on TPU/GPU | Researchers, performance-focused developers |
| MindSpore | Python | AI training & deployment | Developed by Huawei, supports edge-cloud collaboration | Chinese developers, Huawei ecosystem |
| MXNet | Python, Scala, C++ | Deep learning, autodiff | Lightweight, distributed training, AWS support | Developers interested in Gluon API |
| Keras | Python | Prototyping, beginner-friendly modeling | High-level API (on TensorFlow backend), simple and fast | Beginners, quick experimentation |
| PaddlePaddle | Python | Industrial AI | Developed by Baidu, optimized for Chinese NLP, supports distributed training | Chinese-language AI apps, domestic users |
| ONNX | N/A (Model format) | Model interoperability | Standardized format, works across PyTorch, TensorFlow, etc. | Model deployment, framework switching |
| Fastai | Python | Rapid experimentation, education | High-level wrapper over PyTorch, clean API | Students, educators, fast prototyping |
| Detectron2 | Python | Computer vision tasks | Open-sourced by Meta (Facebook), state-of-the-art detection/segmentation models | CV researchers and practitioners |
| Transformers (Hugging Face) | Python | Pretrained NLP models | Huge model zoo (BERT, GPT, LLaMA, etc.), easy to use | NLP developers and fine-tuning enthusiasts |
FAQs of GPU Servers for Deep Learning
The most commonly asked questions about our GPU Dedicated Server for AI and deep learning below:
What's an AI server?
An AI server is a high-performance computer system specifically designed and optimized to handle artificial intelligence (AI) workloads such as: Training deep learning models, Running AI inference tasks, Processing large datasets for machine learning, Serving AI models in production environments.
What is deep learning?
Deep learning is a subset of machine learning and works on the structure and functions similarly to the human brain. It learns from unstructured data and uses complex algorithms to train a neural net.
We primarily use neural networks in deep learning, which is based on AI.
We primarily use neural networks in deep learning, which is based on AI.
What are teraflops?
A teraflop is a measure of a computer's speed. Specifically, it refers to a processor's capability to calculate one trillion floating-point operations per second. Each GPU plan shows the performance of GPU to help you choose the best deep learning servers for AI researches.
What is FP32?
Single-precision floating-point format,sometimes called FP32 or float32, is a computer number format, usually occupying 32 bits in computer memory. It represents a wide dynamic range of numeric values by using a floating radix point.
What GPU is good for deep learning?
The NVIDIA Tesla V100 is good for deep learning. It has a peak single-precision (FP32) throughput of 15.0 teraflops and comes with 16 GB of HBM memory.
What is the best budget GPU servers for deep learning?
The best budget GPU servers for deep learning is the NVIDIA Quadro RTX A4000/A5000 server hosting. Both have a good balance between cost and performance. It is best suited for small projects in deep learning and AI.
Does GPU matter for deep learning?
GPUs are important for deep learning because they offer good performance and memory for training deep neural networks. GPUs can help to speed up the training process by orders of magnitude.
How do you choose GPU servers for deep learning?
When choosing a GPU server for deep learning, you need to consider the performance, memory, and budget. A good starting GPU is the NVIDIA Tesla V100, which has a peak single-precision (FP32) throughput of 14 teraflops and comes with 16 GB of HBM memory.
For a budget option, the best GPU is the NVIDIA Quadro RTX 4000, which has a good balance between cost and performance. It is best suited for small projects in deep learning and AI.
For a budget option, the best GPU is the NVIDIA Quadro RTX 4000, which has a good balance between cost and performance. It is best suited for small projects in deep learning and AI.
What are the advantages of bare metal servers with GPU?
Bare metal servers with GPU 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.
DBM GPU Servers for deep learning are all bare metal servers, so we have the best GPU dedicated server for AI.
DBM GPU Servers for deep learning are all bare metal servers, so we have the best GPU dedicated server for AI.
Why is a GPU best for neural networks?
A GPU is best for neural networks because it has tensor cores on board. Tensor cores speed up the matrix calculations needed for neural networks. Also, the large amount of fast memory in a GPU is important for neural networks. The decisive factor for neural networks is the parallel computation, which GPUs provide.