Installing 6x Tesla V100 GPUs in a Standard Tower Case for Local LLMs
A homelab builder successfully fitted six enterprise Nvidia Tesla V100 16GB GPUs into a standard full-tower PC case instead of a bulky open-frame chassis. The build utilizes an AMD EPYC 7262 processor and an ASRock Rack ROMED8-2T motherboard to run local LLMs. This project shows how individual users can package high-density multi-GPU setups into compact home-friendly enclosures rather than loud server racks. It highlights the practical reuse of affordable legacy enterprise GPUs for local AI inference using combined parallelism techniques. The system provides 96GB of total VRAM across six PCIe V100 cards, with plans to configure model inference using Tensor Parallelism (TP2) and Pipeline Parallelism (PP3). Fitting six enterprise-grade cards into a single PC case introduces significant airflow, power delivery, and physical clearance challenges.
## BACKGROUND
Large language models often exceed the memory capacity of a single GPU, requiring model parallelism to split parameters across multiple cards. Tensor Parallelism divides matrix operations across GPUs for high speed, while Pipeline Parallelism assigns sequential layers of the model to different GPUs. Enterprise cards like the Nvidia Tesla V100 are popular in the homelab community because secondary market prices make multi-GPU memory scaling accessible.