Charles The Cyber Monkey that holds the wire within the fabric of the matrix.

by Ziggy9263 h0lybyte
3 min read


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!


Prioritize GPUs with high CUDA core counts, substantial VRAM (e.g., NVIDIA's A100 or V100 Tensor Core GPUs), and support for hardware acceleration frameworks like NVIDIA's CUDA and cuDNN. Multi-GPU setups or GPU clusters can further boost performance, especially for large-scale training tasks

As of right now the only supplier that we can look at is Nvdia because of their CUDA core integration, thus limiting us to only a couple models:

  • NVIDIA RTX 3090

  • 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.
  • NVIDIA RTX 4090 TI

    • Looks like this might be delayed or may not even come out? I am not too sure.


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.

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.


Logs for Charles



@h0lybyte Updating the general logs for Charles before we begin to build it out!


@h0lybyte We could go with a workstation? server rack? Hmm we have a couple options to pick from.

Short backstory for Charles

Charles The Monkey PC

Charles the Cyber Monkey

Charles was not like the other monkeys. This cyber monkey had a metal arm and a glowing eye that could hack into any system. He lived in a hidden base in the jungle, where he and his friends planned to overthrow the Matrix, the virtual reality that enslaved most of humanity.

One day, Charles received a message from Morpheus, the leader of the resistance. The leader had a mission for him: to infiltrate the main power plant of the Matrix and destroy the wires that connected it to the rest of the world. Charles accepted the challenge and prepared his gear.

He took a hoverbike and flew across the sky, dodging the patrols of the Agents, the guardians of the Matrix. He reached the power plant and landed on the roof. He used his metal arm to hack into the security system and opened a vent. He crawled inside and made his way to the core.

He saw a huge chamber filled with wires that glowed with blue light. He knew that each wire represented a human mind trapped in the Matrix. He felt a pang of pity, but he also knew that this was the only way to free them. He took out a bomb and attached it to one of the wires. He set the timer and ran back to the vent.

He heard an explosion behind him and saw sparks flying everywhere. He smiled and thought that he had done it. He had broken the wires within the Matrix. But then he heard a voice in his head.

“Charles, wake up. You are still in the Matrix.”

He felt a jolt of pain and realized that it was all a trap. He had been tricked by the Agents into destroying a fake power plant. They had used his cybernetic implants to manipulate his senses and make him believe that he was outside.

He opened his eyes and saw that he was lying on a table, surrounded by wires that connected him to a machine. He saw an Agent standing over him, holding a gun.

“Goodbye, cyber monkey,” the Agent said and pulled the trigger.