Maxon C4D

LightWave 3D

Adobe After Effects


The Foundry Modo

The Foundry Nuke

Blender 3D

SideFX Houdini



Drop & Render

My Cloud Render

Bridge Rendering Resources Between Render Farms

With SquidNet’s Bridge interface expand your rendering workload by automatically distributing your render queue jobs to other SquidNet render farms.

  • Automatically transfer render queue backlog to one or more external render farms.
  • Automatic download of rendered content to submitting render farm.
  • Create multiple bridge accounts on bridged render farm server.

Custom Workflows

SquidNet provides a completely customizable framework to support any distributed computing workflow:

  • Command line tools for local and remote access interfaces.
  • Support for custom applications.
  • Support for any distributed computing application that provides an API or command line interface.

 User Login Management Interface

SquidNet provides a complete user management interface. Each account can be configured as an administrator or as a regular user.

  • Secure username and password management support over OpenSSL.
  • Tracking of jobs by username.
  • Analytics based on user account usage.

Distributed Computing

SquidNet’s distributed computing workflow can be customized to distribute process-intensive tasks across any number of networked servers. Custom task submission templates can be created to handle any set of application processing parameters. Any application that supports an API/SDK or command-line interface can be integrated into SquidNet’s workflow engine. For additional information on how to integrate your application, send us an email.

Google Cloud Services

SquidNet provides the internal hooks to connect to Google’s web services. Each Google instance can be used as a local or cloud-based rendering node. Complete integration is on our future road map. If interested in becoming a development partner, just send us an email.

Cross Platform

Support for Windows (7/8/10), Mac OS (Sierra, etc…) and Linux (Ubuntu, Red Hat, Fedora, etc…) Operating Systems. Within each operating system, all graphical user and command line interfaces are identical.  For each platform, there’s just a single installer for the back-end server and a single installer for the front-end Cloud Interface. See download section.

Microsoft Azure Cloud

SquidNet is completely integrated into Microsoft’s Azure Cloud Service. All features available in a local bare-metal render farm are available in the Azure cloud.

  • Automatic inclusion of newly cloned VMs into rendering pipeline.
  • Power Manager Support (WOL, Startup, Shutdown, etc…).
  • Automatic Shutdown of Idle VM Nodes.
  • Multi-region Support within the Azure Cloud.
  • Remote Job Submission from Outside the Azure Cloud.

If interested in becoming a development partner, just send us an email.

 Distributed CPU/GPU Rendering

SquidNet is a network render farm manager that supports both cloud and local CPU/GPU rendering. Cloud (outside the local farm) rendering involves submitting jobs remotely via SquidNet’s Cloud User Interface (CUI). Local farm rendering involves submitting jobs inside the local render farm network using SquidNet’s Local User Interface (LUI). Built-in support for Intel/AMD and Nvidia (CUDA)/AMD (OpenCL).

Free to Educational Institutions

SquidNet provides an instructor/student interface for educational institutions:

  • Multi-student support: Create hundreds of individual student accounts.
  • Remote access to render farm thru cloud rendering interface (CUI)
  • Free 50 render node license for the first year.

 Local Rendering

SquidNet’s local rendering workflow allows for direct submission of CPU/GPU render jobs to a local render farm. Jobs can be submitted using the local job submission interface or from a scriptable command line interface. Standard job management operations (suspend, resume, cancel, etc…) are available from both the GUI and command line interfaces.

Amazon Web Services

SquidNet provides the internal hooks to connect to Amazon’s web services. The node management interface between local bare-metal render nodes and Amazon instances is seamlessly integrated into the rendering pipeline. Complete integration is on our future road map. If interested in becoming a development partner, just send us an email.

Machine Learning

SquidNet’s GPU interface provides an ideal candidate for machine learning distributed operations. The front-end local and cloud-based user interfaces provide all the user input data to the back-end GPU processing nodes. When processing is completed, SquidNet bundles up the output content and downloads it to the user’s desktop automatically. Send us an email if you’re interested in becoming a development partner for our machine learning interface design.

Sublease Your Render Farm Resources

With SquidNet’s cloud rendering interface you can lease your idle render farm time to artists and animators outside of your local studio. Use the application licenses already on your local farm to let remote users take advantage of the applications that you already support.

  • Manage one more customer accounts.
  • Add multiple users to each customer account.
  • Assign rendering credits to each customer account.

Send us an email for more information.

Remote User Desktop Application

SquidNet’s Cloud User Interface (CUI) provides for access to your jobs from your local desktop. Features include:

  • Interface to in-application plugins.
  • Automatic uploading of rendering content.
  • Job monitoring and control.
  • Automatic downloading of rendered frames.

Built-in Render Farm Service Management Interface

SquidNet provides a complete render farm service manager framework. Create studio profiles with one or more user login accounts. Create rendering leases that determine studio’s rendering criteria, settings and limitations. Studio accounts are assigned render credits which are used to regulate usage of render farm resources.

Cloud Rendering

SquidNet’s cloud rendering workflow allows for direct submission of CPU/GPU jobs from outside the local render farm network. Job submissions can be submitted from a desktop application or from a scriptable command line interface. All communications between remote and local connection are securely protected with OpenSSL AES 256 bit encryption.

Extensible Interface

SquidNet’s extensible interface can support any compute-intensive task that can be distributed across hundreds of parallel processing nodes. These tasks can include: CPU/GPU animation rendering, virtual reality scene processing, data mining, analytical processing and scientific experimentation. Custom job templates provide users with the flexibility to manage their custom applications across their computer network. Support for any application that provides an sdk/api or command line interface.