AI ToolsJul 13, 2026, 2:05 PM

I benchmarked 15 "E-Waste" GPUs with Modern Workloads

30-second summary

A Reddit user has benchmarked 15 decommissioned NVIDIA enterprise GPUs with modern workloads, revealing potential cost-effective VRAM options for homelabs.

TickrWire
I benchmarked 15 "E-Waste" GPUs with Modern Workloads
Key takeaways
  • Decommissioned NVIDIA enterprise GPUs can be repurposed for modern workloads.
  • These old cards can provide a cost-effective VRAM solution for homelabs.
  • The P100 (16GB) and V100 (16GB) GPUs can be purchased for around $75 and $200 respectively.
Full story

Researchers and developers often seek cost-effective solutions for their projects. A Reddit user has taken an innovative approach by repurposing decommissioned NVIDIA enterprise GPUs for modern workloads. The user spent a year building custom GPU coolers and a benchmarking tool to test the potential of these old cards.

The results show that GPUs like the P100 (16GB) and V100 (16GB) can be purchased for around $75 and $200 respectively, making them a viable option for those seeking affordable VRAM. Combined with dirt-cheap X99 Xeon motherboards, these decommissioned GPUs can provide a massive source of idle VRAM for homelabs.

This development highlights the potential for creative repurposing of old technology, offering a sustainable and budget-friendly solution for those in need of VRAM resources.

Why this matters
Developers

This development offers a sustainable and budget-friendly solution for VRAM resources.

Students

This approach can provide affordable access to VRAM resources for students working on projects.

Glossary
VRAM
Video Random Access Memory, a type of memory used for graphics rendering.
Sources · 1
Read next
More stories
TickrWireAI News Intelligence

We aggregate, verify, summarise and explain the latest artificial intelligence news from open, legal sources.

Daily AI digest

Top AI stories, summarised, in your inbox each morning.

© 2026 TickrWire. Summaries and analysis are AI-generated and may contain errors.