Quick Check
System requirements
The only mandatory requirement for using MiningMath is a 64-bits system. Other minimum requirements are listed further:
Windows 10
64-bits system (mandatory)
110 MB of space (installation) + additional space for your projects' files.
Processor: processors above 2.4 GHz are recommended to improve your experience.
Memory: at least 8 GB of RAM is required. 16 GB of RAM or higher is recommended to improve your experience.
OpenGL 3.2 or above. Discover yours by downloading and running the procedure available here.
Visual C++ Redistributable: Installation of Visual C++ Redistributable is necessary to run this software.
Recommended Hardware
Memory should be a higher priority when choosing the machine in which MiningMath will be run on. Here’s a list of priority upgrades to improve performance with large scale datasets:
Higher Ram
Higher Ram frequency
Higher processing clock
Optimizing memory for MiningMath
RAM is one of the most important components to consider when choosing a computer for running MiningMath, especially since Windows alone consumes a significant amount of memory.
If you experience an insufficient memory warning or a sudden crash while using MiningMath, here are some steps to improve performance:
- Upgrade Your RAM: if possible, increasing your RAM is the most effective way to enhance performance. The recommended specifications are listed in the “Recommended Hardware” section. Based on our experience with complex projects, 64 GB is sufficient for most cases.
- Free Up Memory: close any unnecessary applications running in the background to free up RAM while using MiningMath.
- Increase Windows Virtual Memory: you can allocate disk space to be used as additional RAM. For step-by-step instructions, follow this tutorial.
- Reblock the Model: if memory issues persist, consider reblocking to reduce the model size. More details can be found here.
Additional tip: in rare cases, when working with boxes, adjusting block coordinates to bring them closer together can create a smaller model box, improving performance.
