● Covered by 1 source · 1 reportLow impact1 neutral
Enthusiast Integrates Nvidia Tesla V100 into Gaming PC for LLM Inference
Key points
- Nvidia Tesla V100 SXM2 added to a gaming PC for $266.
- Total VRAM increased to 32GB (16GB RTX 4080 + 16GB Tesla V100).
- Runs a 27 billion parameter LLM at 32 tokens per second locally.
- Required an SXM2-to-PCIe adapter and a PWM fan modification.
Hardware Integration for Increased VRAM
A computing enthusiast, Oscar Molnar, integrated an Nvidia Tesla V100 SXM2 GPU with 16GB HBM2 into an existing gaming PC. This addition doubled the system's total VRAM to 32GB, combining the VRAM from an RTX 4080 and the newly added Tesla V100. The total cost for the Tesla V100 and necessary components was $266.
Overcoming Integration Challenges
The integration required sourcing an SXM2-to-PCIe adapter for approximately $66 to connect the enterprise GPU to the consumer motherboard. A significant challenge was the high noise output of the Tesla V100's stock cooler, which measured 82dB. This was addressed by a PWM modification, rerouting the fan wires to the motherboard's PWM fan header, allowing for quieter operation at 10% fan speed while maintaining temperatures below 50C under full load.
LLM Performance and Configuration
With the hardware configured, the system achieved local inference of a 27 billion parameter large language model at a rate of 32 tokens per second. This performance is described as sufficient for interactive use and faster than many cloud API alternatives. The setup utilized NixOS with a legacy Nvidia driver that supports both the Volta (Tesla V100) and Ada (RTX 4080) architectures.
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Reporting from
An enthusiast added a used Nvidia Tesla V100 GPU to a gaming PC, increasing total VRAM to 32GB for $266, enabling local inference of a 27 billion parameter LLM at 32 tokens per second. This modification demonstrates a cost-effective method for enhancing local AI model processing capabilities using older enterprise hardware.