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Reddit User Buys Used Crypto Mining Rig with 54GB VRAM for $35

🔄 Updated 26d ago
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Key points

  • Crypto mining rig with 54GB VRAM purchased for $35.
  • Rig contains nine Nvidia P106-100 GPUs.
  • Successfully ran Qwen3.6-35B-A3B model at 30+ tokens/second.
  • GPUs are Pascal generation, with limited modern software support.

Acquisition of a Rusty Rig

A Reddit user acquired an old crypto mining rig for approximately $35 (3,000 rubles) from a seller on Avito, a Russian classifieds site. The rig was discovered in a damp garage and its functionality was unknown at the time of purchase, as the seller refused a pre-purchase test.

Unexpected Functionality and VRAM Capacity

Despite its condition, all nine Nvidia P106-100 GPUs on the rig were functional upon the first attempt. These GPUs collectively provide 54GB of VRAM, which the user obtained at a cost of about 65 cents per gigabyte. The P106-100 cards are Pascal-generation GPUs, each with 6GB of GDDR5 memory.

AI Model Testing and Performance

The user successfully tested the rig by running the 35-billion-parameter Qwen3.6-35B-A3B model in Ollama. The model operated at over 30 tokens per second, indicating the rig's capability for certain AI workloads. The performance is expected to be limited by the weak Celeron CPU and PCIe 2.0 x1 motherboard slots.

GPU Specifications and Software Support

The Nvidia P106-100 GPUs, comparable to GTX 1060 6GB cards, were released around mid-2017. Modern CUDA versions (13.0 and above) no longer support offline compilation for Pascal GPUs, and driver branch 580 is the last to support GPUs before compute capability 7.5. Security updates for these cards are supported until October 2028.

✨ This summary was generated by AI from the outlets' reporting listed below. It is not independently verified and may contain errors — check the original sources. How BrevFeed works →

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Reporting from

A Reddit user purchased a used crypto mining rig containing nine Nvidia P106-100 GPUs, totaling 54GB of VRAM, for $35. The rig, found in a damp garage, successfully ran a 35-billion-parameter AI model at over 30 tokens per second, demonstrating a low-cost option for accessing significant VRAM for AI tasks.