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Picture a symphony of GPUs, each singing in computational harmony within a single machine. This ensemble isn’t just for show; it’s tailored for the demanding concertos of machine learning. In this digital orchestra, every GPU strikes a note, turning data into artful insights!


These are the hardware notes for charles, our internal machine learning build/project.


NVIDIA RTX 4090 - The 4090 series has a couple different variants on the market, but since we want to cluster them together, it might make more sense to look at liquid cooled instances.

The 4090 that we are using right now is the Suprim LiquidX from MSI.


While the required RAM for constructing a GPU cluster for deep learning varies based on specific use-cases and requirements, we’ll outline key considerations to guide your decision-making process.

The RAM sticks we are using are DDR5.

Dataset Size

When preprocessing and loading large datasets into main memory, a substantial amount of RAM is crucial for fast access, as it’s quicker than persistent storage. Standard memory configurations may fall short for extensive datasets. Therefore, 128GB, 256GB, or even more RAM could be essential, especially with on-the-fly data augmentation or transformation.

Concurrent Tasks

Running concurrent tasks like data preprocessing, serving models, or multiple training jobs on one machine demands ample RAM. Sufficient memory ensures efficient multitasking and optimal performance during simultaneous operations.

GPU-System Communication

Adequate system RAM is essential to prevent bottlenecks when data is transferred between the CPU and GPU. Remember that while GPU memory (VRAM) is crucial for model training, the system RAM plays a role in staging and preparing data.

Software Overhead

Running the operating system, deep learning frameworks, databases, and other necessary software tools will also consume RAM.

Scaling Strategy

If your cluster, managed through solutions like Docker and Kubernetes, is designed to distribute tasks across multiple nodes, each node might not require a vast amount of RAM. Instead, RAM can be allocated according to the specific role and demand of each node, enhancing your scaling strategy.


  • Entry-Level Configuration: At least 32GB to 64GB of RAM per node.
  • Mid-Range Configuration: Between 64GB to 128GB of RAM per node.
  • High-End Configuration: 256GB or more per node, especially if you’re working with vast datasets or complex multi-stage workflows.

Remember to always tailor your cluster’s configuration to your specific needs. Monitoring tools can help gauge memory usage in real-time and assist in making informed decisions about future upgrades.



The setup for the Charles will be in our Proxmox Applications page.


This section will be for the machine learning and ai eco-system + notes.


This section will be for our gaming section.