Enhance your project with Compute Servers
Compute servers enhance your CoCalc project
Extend your project's compute capabilities far beyond the bounds of its underlying compute environment.
Configure the remote compute servers exactly to your needs
- CPU: you can not only select the number of CPU cores, but also the type of machine.
- Memory: depending on the type of machine, select from the full range of possible memory configurations.
- GPU: select one or more GPUs for your selected machine
- Disk: configure the size and speed of the provisioned disk
- Hosting: choose a subdomain, in order to host any kind of web application
- Use the Google Colab Softwar Environment with a GPU
- Use the official PyTorch image with a GPU
- Use the Mathematica Jupyter Kernel
- Use Ollama with a nice web UI to run Large Language Models using GPUs
- Use a large number of CPUs and RAM to run resource intensive computations in parallel using R, SageMath, etc.
- Run your own custom CoCalc server or Sage Cell Server anywhere in the world.
Compute Server Functionality
More details about compute servers
Compute servers have a quick startup time. Pre-configured Docker images are already pulled into the virtual machine. You neither have to wait a longtime to provision the machine, nor do you have to wait for preparing and installing the ncessary software environment.
TestCalc makes switching between the local compute environment and the remote compute server very easy.
The files in your project are synchronized with the compute server, which eliminates any headaches provisioning storage and transferring files back and forth.
As part of configuring the remote server, you can tune which folders are excluded from synchronization, select additional scratch storage space, and also configure the size of the remote storage disk.
At the end of using the compute machine, you can either stop it to preserve the data, or delete it to save the cost of keeping the stored files around.
You can create VM's with over 10TB of RAM, over 400 cores, and up to 65TB of disk space.
You can choose one or more T4, L4, and A100 GPU's.
Many preconfigured software stacks are available, including PyTorch, Tensorflow, Google Colab, CUDA, SageMath, Julia, and R.
You can easily compare prices in different regions across the world, and get the best spot instance deals, or select low CO2 data centers. Compute servers have a cached networked filesystem, so you can take advantage of much better global rates, rather than being stuck in one region.
You can dynamically enlarge your disk at any time, even while the server is running, and the OS will automatically enlarge the available space.
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