Overview

Initially, we evaluated the hardware requirements for running LLaMA:3.1-405B-fp16 and Deepseek-V3-671B-fp16 models. The hardware specifications were extremely high and unfeasible for our setup:

• LLaMA:3.1-405B-fp16: Required significant computational power.
• Deepseek-V3-671B-fp16: Demanded even higher resources.

Given these impractical requirements, we opted for a more manageable model—LLaMA:3-8B-fp16.

New Model Decision: LLaMA:3-8B-fp16

After re-evaluating the project’s needs and considering available resources, we decided to use LLaMA:3-8B-fp16, which requires 16 GB of graphics memory. Additionally, the Facebook Zero Shot model requires 2 GB of VRAM, bringing the total requirement to 18 GB of VRAM.

GPU Decision: NVIDIA RTX 4090

To meet these new requirements, we chose the NVIDIA RTX 4090, which provides 24 GB of VRAM. This model not only covers our current needs but also leaves additional memory capacity for future scalability.

Why RTX 4090?

• 24 GB VRAM:
  Easily handles both the LLaMA:3-8B-fp16 (16 GB) and Facebook Zero Shot model (2 GB) with room to spare for future workloads.

  Example: With 24 GB of VRAM, we can run both models simultaneously, processing large datasets without memory limitations.

• Future-Proofing:
  The extra VRAM allows for potential future upgrades or additional AI models as the project expands.

Next Steps

• Install and Configure RTX 4090.
• Test the models to ensure they perform optimally with the new hardware.
• Monitor VRAM usage to evaluate potential scaling needs in future projects.