How I Used NVIDIA GPUs to Keep Myself Warm During Peak German Winters

Life in Germany at the MindGarage AI Lab

Back in 2016, I was part of an AI lab called MindGarage, led by Markus Liwicki. I practically lived in that lab—sleepless nights spent training AI models, experimenting with text-to-image generation, and even praying to the AI Gods for a better machine learning framework (because, frankly, TensorFlow just wasn’t cutting it then; thank goodness PyTorch came along in September 2016).

I had my sleeping bag, a lab fridge for leftovers, and some nearby Indian friends who would occasionally feed me rotis and wonderful curries. Coming from a coastal Indian city, the brutal German winters were a shock. The lab was vast and cold at night; the central heating only ran for a few hours, leaving me to fend off the chill like a true hobo. And that’s when I discovered an unlikely solution.

The Unconventional Heater: NVIDIA GPUs

Running the Machines at Full Throttle

We had 8 NVIDIA GPUs scattered across workstations—including some Tesla P100s (which, back in the day, were among the best for deep learning workloads).

Note: The Tesla P100 is designed for data center and AI applications and typically has a TDP of around 250W (see NVIDIA Tesla P100 Specifications).

When these GPUs run at full throttle (nearly 100% utilization), they generate a tremendous amount of heat. Although each GPU is equipped with advanced cooling systems (vapor chambers, fans, etc.), these systems don’t eliminate the heat—they merely transfer it from the chip to the ambient air.

The Science Behind GPU Heating

In essence, every watt of power consumed by a GPU eventually becomes heat. Whether it’s the sophisticated cooling of a Tesla P100 or the robust designs of later-generation GPUs, the underlying physics remains the same: electrical energy converts to thermal energy.

Even the best cooling systems are designed to protect the hardware by quickly moving heat away from sensitive components. However, if you were to capture all that expelled heat in a sealed environment, the ambient temperature would rise dramatically.

The Math Behind the Heat

Let’s break down the numbers using our Tesla P100 as an example:

Calculating Total Heat Energy

Power per GPU: approximately 250W

Total for 8 GPUs: 8 x 250W = 2000W

Energy generated in 1 hour (3600 seconds):

Energy = 2000W x 3600s = 7,200,000 Joules

Estimating Temperature Increase in a Sealed Room

Assume a small, sealed lab with a volume of 40 cubic meters. The mass of the air in that room can be calculated using an air density of approximately 1.225 kg per cubic meter:

Mass of air = 40 m³ x 1.225 kg/m³ = 49 kg

Specific heat capacity of air is about 1005 Joules per kg per °C.

Energy needed to raise the temperature by 1°C = mass x specific heat = 49 kg x 1005 J/(kg·°C) = about 49,245 Joules per °C

Therefore, the theoretical temperature increase (ΔT) is:

ΔT = Total Energy / (mass x specific heat) = 7,200,000 Joules / 49,245 Joules per °C ≈ 146°C

Caveat: This calculation assumes a perfectly sealed (adiabatic) environment where no heat escapes—a theoretical worst-case scenario. In real labs, ventilation and active cooling drastically reduce the temperature rise.

Real-World Considerations

Even with effective GPU cooling, the heat isn’t destroyed—it’s simply moved away from the chip. In my lab, the cooling systems kept the GPUs at safe operating temperatures, but the expelled heat warmed the ambient air. I found that by carefully managing the workload (and thereby the GPU utilization), I could maintain a cozy lab temperature throughout the night.

A fun observation: tweaking hyperparameters (such as batch size and model size) even influenced the GPU utilization and, by extension, the heat output. Consistent GPU load meant a steady, warm environment, while brief lapses in utilization resulted in a slight temperature dip. Whether that difference was purely physical or just my inner nerd noticing it, it made those winter nights memorable.

Conclusion

Training AI models on NVIDIA GPUs wasn’t just about advancing technology—it became my unconventional heating system during those harsh German winters. Although the GPUs’ cooling systems ensured the hardware stayed cool, the inevitable byproduct was heat. In our controlled (albeit theoretical) scenario, the math reveals just how potent that heat production can be. More importantly, it was incredibly fun—and surprisingly effective—in keeping me warm and helping me sleep better.

Expanding Your AI Journey with Ayushman

If you're intrigued by the unconventional ways AI can integrate into daily life, you might find these experiences insightful:

• Pursuing AI Education Abroad: Discover how I completed a Master's in AI in Germany with minimal savings and no study loan.

• Building an AI Startup in Odisha, India: Learn about my journey from Canada to Bhubaneswar to establish an AI startup, challenging the notion that innovation is confined to major tech hubs.

• Embracing Perseverance in Tech: Read about how hard work and resilience redefined my path as an "average" engineer, turning challenges into strengths.

For more insights and stories on AI integration and leadership, explore the AI with Ayushman blog.

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Sources

NVIDIA Tesla P100 Specifications, NVIDIA.

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