Cost-effective
Keras GPU Plans & Pricing
Basic GPU Dedicated Server - RTX 4060
- 64GB RAM
- Eight-Core E5-2690
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce RTX 4060
- Microarchitecture: Ada Lovelace
- CUDA Cores: 3072
- Tensor Cores: 96
- GPU Memory: 8GB GDDR6
- FP32 Performance: 15.11 TFLOPS
Basic GPU Dedicated Server - RTX 5060
- 64GB RAM
- 24-Core Platinum 8160
- 120GB SSD + 960GB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia GeForce RTX 5060
- Microarchitecture: Blackwell 2.0
- CUDA Cores: 4608
- Tensor Cores: 144
- GPU Memory: 8GB GDDR7
- FP32 Performance: 23.22 TFLOPS
Advanced GPU Dedicated Server - RTX 3060 Ti
- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: GeForce RTX 3060 Ti
- Microarchitecture: Ampere
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Advanced GPU Dedicated Server - A4000
- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A4000
- Microarchitecture: Ampere
- CUDA Cores: 6144
- Tensor Cores: 192
- GPU Memory: 16GB GDDR6
- FP32 Performance: 19.2 TFLOPS
Advanced GPU Dedicated Server - A5000
- 128GB RAM
- Dual 12-Core E5-2697v2
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A5000
- Microarchitecture: Ampere
- CUDA Cores: 8192
- Tensor Cores: 256
- GPU Memory: 24GB GDDR6
- FP32 Performance: 27.8 TFLOPS
Advanced GPU Dedicated Server - V100
- 128GB RAM
- Dual 12-Core E5-2690v3
- 240GB SSD + 2TB SSD
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia V100
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
- Cost-effective for AI, deep learning, data visualization, HPC, etc
Multi-GPU Dedicated Server - 3xRTX 3060 Ti
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 3 x GeForce RTX 3060 Ti
- Microarchitecture: Ampere
- CUDA Cores: 4864
- Tensor Cores: 152
- GPU Memory: 8GB GDDR6
- FP32 Performance: 16.2 TFLOPS
Enterprise GPU Dedicated Server - RTX A6000
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Multi-GPU Dedicated Server - 3xV100
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 3 x Nvidia V100
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
- Expertise in deep learning and AI workloads with more tensor cores
Enterprise GPU Dedicated Server - A100
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 100Mbps-1Gbps
- OS: Windows / Linux
- GPU: Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
- Good alternativeto A800, H100, H800, L40. Support FP64 precision computation, large-scale inference/AI training/ML.etc
Multi-GPU Dedicated Server - 3xRTX A6000
- 256GB RAM
- Dual 18-Core E5-2697v4
- 240GB SSD + 2TB NVMe + 8TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 3 x Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Multi-GPU Dedicated Server - 4xRTX A6000
- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 4 x Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
Multi-GPU Dedicated Server - 8xV100
- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 8 x Nvidia Tesla V100
- Microarchitecture: Volta
- CUDA Cores: 5,120
- Tensor Cores: 640
- GPU Memory: 16GB HBM2
- FP32 Performance: 14 TFLOPS
Multi-GPU Dedicated Server - 4xA100
- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 4 x Nvidia A100
- Microarchitecture: Ampere
- CUDA Cores: 6912
- Tensor Cores: 432
- GPU Memory: 40GB HBM2
- FP32 Performance: 19.5 TFLOPS
Multi-GPU Dedicated Server - 8xRTX A6000
- 512GB RAM
- Dual 22-Core E5-2699v4
- 240GB SSD + 4TB NVMe + 16TB SATA
- 1Gbps
- OS: Windows / Linux
- GPU: 8 x Quadro RTX A6000
- Microarchitecture: Ampere
- CUDA Cores: 10,752
- Tensor Cores: 336
- GPU Memory: 48GB GDDR6
- FP32 Performance: 38.71 TFLOPS
How to Install Keras with GPU
Requirement for Keras Installation
Step-by-Step Instructions of Keras
# Sample: conda create --name tf python=3.9
# Sample: pip install --upgrade pip pip install tensorflow
# If a list of GPU devices is returned, you've installed TensorFlow successfully. import tensorflow as tf; print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) from tensorflow import keras
6 Reasons to Choose our Keras GPU Servers
Dedicated GPU Cards
Full Root/Admin Access
99.9% Uptime Guarantee
NVIDIA CUDA
Customization
Advantages of Deep Learning with Keras GPU
User-Friendly and Fast Deployment
Quality Documentation and Large Community Support
Easy to Turn Models into Products
Multiple GPU Support
Multiple Backend and Modularity
Pre-Trained models
Features Comparison: Keras vs PyTorch vs TensorFlow
Features | Keras | TensorFlow | PyTorch | MXNet |
---|---|---|---|---|
API Level | High | High and low | Low | Hign and low |
Architecture | Simple, concise, readable | Not easy to use | Complex, less readable | Complex, less readable |
Datasets | Smaller datasets | Large datasets, high performance | Large datasets, high performance | Large datasets, high performance |
Debugging | Simple network, so debugging is not often needed | Difficult to conduct debugging | Good debugging capabilities | Hard to debug pure symbol codes |
Trained Models | Yes | Yes | Yes | Yes |
Popularity | Most popular | Second most popular | Third most popular | Fourth most popular |
Speed | Slow, low performance | Fastest on VGG-16, high performance | Fastest on Faster-RCNN, high performance | Fastest on ResNet-50, high performance |
Written In | Python | C++, CUDA, Python | Lua, LuaJIT, C, CUDA, and C++ | C++, Python |
Quickstart Video - Keras Tutorial For Beginners
FAQs of Keras GPU Server
What Keras is used for?
Why do we need Keras?
It offers consistent & simple APIs.
It minimizes the number of user actions required for common use cases.
It provides clear and actionable feedback upon user error.
Is Keras better than PyTorch?
Does Keras automatically use GPU?
What is Keras GPU?
Do I need to install Keras if I have TensorFlow?
When do I need GPUs for Keras?
If you're just learning Keras and want to play around with its different functionalities, then Keras without GPU is fine and your CPU in enough for that.
What are the best GPUs for Keras deep learning?
Feel free to choose the best plan that has the right CPU, resources, and GPUs for Keras.
How can I run a Keras model on multiple GPUs?
How can I run Keras on GPU?
If you are running on the Theano backend, you can use theano flags or manually set config at the beginning of your code.
What are the advantages of bare metal GPUs for Keras?
DBM GPU Servers for Keras use all bare metal servers, so we have best GPU dedicated server for AI.
TensorFlow vs Keras: Key Differences Between Them
2. Keras is perfect for quick implementations, while Tensorflow is ideal for Deep learning research and complex networks.
3. Keras uses API debug tools, such as TFDBG. On the other hand, in Tensorflow, you can use Tensor board visualization tools for debugging.
4. Keras has a simple architecture that is readable and concise, while Tensorflow is not very easy to use.
5. Keras is usually used for small datasets, but TensorFlow is used for high-performance models and large datasets.
6. In Keras, community support is minimal, while in TensorFlow, it is backed by a large community of tech companies.
7. Keras is mostly used for low-performance models, whereas TensorFlow can be used for high-performance models.