Accelerate data processing
Process large videos & images with ease or run GPU-intensive Machine Learning models.
GPU Render Instances
Dedicated Tesla P100s for all your Machine Learning & Artificial Intelligence needs.
Container-ready
Deploy GPU nodes directly from Kubernetes Kapsule or use the NVIDIA Container Toolkit.
Easy to use
Two pre-loaded Ubuntu distributions for Machine Learning.
Technical specifications
- gpu
GPU
Dedicated NVIDIA Tesla P100 16GB PCIe
- processor_frequency
Processor Frequency
2.40 GHz
- memory
Memory
42GB
- bandwidth
Bandwidth
1 Gbit/s
- processor
Processor
10 Intel Xeon Gold 6148 cores
- gpu_memory
GPU Memory
16GB CoWoS HBM2
- memory_type
Memory type
DDR4-2666
- storage
Storage
Local Storage or Block Storage on demand
Use cases
Artificial Intelligence & Machine Learning
GPU Instances have been designed to train complex models at high speed so you can improve your algorithms’ predictions and decisions. The dedicated NVIDIA Tesla P100 makes them particularly well-suited for Neural Networks and Deep Learning applications.
GPU-accelerated data processing
GPU Instances allow you to manipulate large datasets and extract the meaningful information you are looking for at high speed. They help data scientists summarize and classify non-structured data.
Video encoding & 3D rendering
GPU Instances can speed up ultra-high-definition video encoding and render 3D models at high speed. Optimize the cost and duration of your post-production needs, whether they are one-off or regular.
A complete cloud ecosystem
Grow flexibly with a no lock-in European cloud
The most complete cloud ecosystem in Europe, from Bare Metal to serverless and everything in between.
Excellent price/performance ratio
Our wide range of services is designed to sustain your growth cost-effectively, at all stages of development.
Leverage multi-cloud solutions
Our products are compatible with market standards so that you can enjoy the freedom of no lock-in.
Sustainable by design
100% of electricity consumed in our data centers comes from renewable energy. Decommissioned hardware is securely reused & recycled.
Get started with tutorials
How to create your first GPU InstanceGo to the tutorial
How to set up and configure Jupyter Notebook on a GPU InstanceGo to the tutorial
Frequently asked questions
What are the advantages of NVIDIA P100 GPUs?
The NVIDIA Pascal architecture enables the Tesla P100 to deliver superior performance for HPC and hyperscale workloads. With more than 21 teraFLOPS of 16-bit floating-point (FP16) performance, Pascal is optimized to drive exciting new possibilities in Deep Learning applications. Pascal also delivers over 5 and 10 teraFLOPS of double- and single-precision performance for HPC workloads.
What is included in an Scaleway “Ubuntu ML” image?
Our Ubuntu ML (for Machine Learning) images are Ubuntu bionic images pre-packaged with the most popular tools, framework and libraries, such as Cuda, Conda, TensorFlow, Keras, RAPIDS, JAX, and several NLP and visualization tools.
Am I required to use a Scaleway "Ubuntu ML" image?
In addition to “Ubuntu ML” images, you can use almost every other image that Scaleway provides for General Purpose Instances. You can also bring your own images.
If you want to use our “Ubuntu ML” images without Conda, you can save some disk space with the invit conda deactivate, then conda env remove -n ai.