Our platform can be used for High performance computing jobs such as:

• Artificial Intelligence
• Computational Fluid dynamics
• 3d simulations
• 3d rendering
• Big Data analytics

Q Blocks helps Machine learning engineers and data scientists easily spin up instances on remote GPU powered workstations.

As a user, you can easily pre-configure the instances with AI frameworks such as Tensorflow, PyTorch, Keras and then run scripts using Jupyter Notebooks or CLI interface to quickly build, train and deploy AI models.

There are several advantages that you get on using Q Blocks such as:

• Upto 80% Less Expensive:
  Yes, you read that right. GPU instances on Q Blocks are very inexpensive when compared to primitive cloud companies.
• Easy to use:
  We have made it super easy for a user to quickly fire up a GPU instance and start processing in a matter of few minutes.
• Pre-configured instances:
  Select your favorite AI frameworks to get a pre-configured instance ready for deep learning processing or machine learning inference.

When you select a GPU powered machine on our network, you create an instance. You can configure your instance with AI frameworks such as Tensorflow, Keras, PyTorch etc. and choose storage required in your instance.

We use Stripe Payment Gateway to securely transact on our website. You can create a Payment profile and then either add credits or select a package depending on your requirement. Packages are heavily discounted when compared to pay as you go plan.

Once you purchse a plan, you get the equivalent dollar value worth of credits that you can use to spin up computing instances on Q Blocks. Credits are deducted on a per-minute billing bases. So if you launch an instance, your credits will be deducted for the total number of minutes the instance was ON.

In your dashboard, you will see Active Instances tab in the left side menu. On Active Instances page, you can see a list of all your active instances.

Along with every instance, you will see 3 action buttons that will help you to:

• Connectto your instance. Use this button to connect to your Jupyter lab or get SSH command for CLI interface.
• Stopan instance. Stopping an instance will stop your compute billing, but you will be charged for storage.
• Destroyan instance. Destroying an instance will terminate your instance, delete your storage and stop your billing altogether.

When you terminate your instance, your data is deleted from the machine. Thus, it is not accessible once the instance is terminated. So we would request that you create regular backups of your data locally or on a cloud storage service of your choice.

We don't offer refund on the purchases. They are final and as it is.

Q Blocks helps machine learning engineers and data scientists rent out GPU systems on our network. By hosting your GPU machines on our website, you earn an amount for every hour your machine is rented on the network.

Basic requirements for a machine are:

• An Nvidia GPU (Supported GPUs: GTX 10XX series, RTX 20XX / 30XX series, Titan Series, Tesla Series)
• Ubuntu 18.04 or above
• 16 GB RAM or more per GPU
• 250 GB or more SSD Storage per GPU
• 4 CPU cores per GPU

Apart from the above system requirements, a high speed internet and consistent availability of the machine is a must to get a strong ROI while we can offer a sustainable service on your machine to our users.

On connecting your machine to Q Blocks network, the computer resources are aggregated and displayed on the website.

So when a user requests a GPU instance from their dashbaord, then a containerized virtual package of GPU, CPU, RAM and some storage is provided to the user. These virtual instances are created in your machine and the users will stay in their isolated virtual instances.

Currently supported workloads include but are not limited to Machine learning training, 3D design, Scientific simulations for the users on Q Blocks website.

Q Blocks is a Techstars funded Canadian Company. Techstars is a global startup accelerator that has supported innovative startups in past such as Sendgrid and Digital Ocean.

At Q Blocks, we are on a mission to build the world's biggest supercomputer by connecting 1000s of under-utilized GPU powered computing resources throughout the globe for enabling scientific research at unprecendented scale.

We want to usher the world into this new revolution of personal supercomputing. And by becoming a provider on Q Blocks, you are not just entitled to the ROI of your machine but also to propel this technology forward.

Also, since we provide containerized virtual instances to users, your data on the machine remains safe as no Q Blocks user can access resources outside their instance. Thus, nobody can access your data or resources other than provided by you.

Q Blocks however does request you as a host to keep a regular check on system vitals and temperatures to make sure that the system remains under normal working conditions.

Earnings are dependant on a few factors namely:

• Compute Capability of your system (Majorly dependant on GPU power)
• Number of hours somebody rents your resources on the network.
• Dedicated resource provided by you or not

On average, a host on Q Blocks network earns anywhere between USD 50 - 350 per month for full month usage. To know the potential earning for your system, reach out to us at support@qblocks.cloud with your system specs and we would be happy to assist you.