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Use your Nvidia GPU's VRAM as swap space on Linux

GitHub - c0deJedi/nbd-vram: Use your NVIDIA GPU's VRAM as swap space on Linux. Built for laptops with soldered memory and no upgrade path. If you have an RTX card sitting there with 8GB of VRAM and you're getting swapped to SSD, this puts that VRAM to work

github.com

June 2, 2026

5 min read

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66/100

Summary

c0deJedi's nbd-vram enables the use of NVIDIA GPU VRAM as swap space on Linux, particularly for laptops with soldered memory. This solution allows high-priority swap allocation, significantly increasing total addressable memory by utilizing unused VRAM.

Key Takeaways

  • The nbd-vram project allows users to utilize NVIDIA GPU VRAM as swap space on Linux, particularly for laptops with soldered memory and no upgrade path.
  • The implementation uses a small daemon that allocates VRAM via the CUDA driver API and exposes it as a block device using the Network Block Device (NBD) protocol.
  • The system can achieve a total addressable memory of approximately 46 GB by combining RAM, VRAM, zram, and SSD swap.
  • The service automatically starts on boot and can be configured to manage power-aware settings based on AC power status.
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Community Sentiment

Mixed

Positives

  • Utilizing VRAM as swap space can optimize resource usage, particularly for systems with limited RAM, enhancing performance for specific applications.
  • The concept of GPU-accelerated databases is gaining traction, with major companies investing in this technology, indicating a strong future for data processing capabilities.
  • This approach could allow users with high-end GPUs to leverage their idle VRAM for other tasks, potentially increasing overall system efficiency.

Concerns

  • The reported sequential throughput of ~1.3 GB/s on an RTX 3070 is significantly underwhelming compared to its potential, raising concerns about performance limitations.
  • Swapping to NVMe drives may be faster but introduces higher latency, which could negate the benefits of using VRAM as swap space.
  • There are worries about the implementation of this system, as it may not be optimized for high performance, leading to inefficiencies.