I recently came across a Reddit post that left me wondering – how is it possible to load a massive 20 billion parameter model like GPT-oss-20b on a GPU with only 16 GB of VRAM? The math just doesn’t seem to add up. With each parameter taking up 2 bytes of space, we’re talking about a whopping 39.1 GB of VRAM required just to load the model. That’s way more than the available VRAM on most consumer-grade GPUs.
The original poster assumed that the model would move the expert weights to the GPU for each forward pass, but that’s not an efficient approach. So, what’s the secret to loading this massive model on a relatively modest GPU?
After digging deeper, I found the answer in the model’s use of quantization. By reducing the precision of the model’s weights, we can significantly reduce the amount of memory required to store them. In this case, the model uses 4-bit quantization, which allows it to fit on a GPU with much less VRAM.
But how does this impact performance? Surprisingly, the 4-bit quantization doesn’t seem to affect the model’s performance too much. In fact, it’s able to achieve performance on par with other models like DeepSeek R1, which has 671 billion parameters.
If you’re curious about how GPT-oss-20b works its magic, I recommend checking out the model’s GitHub repository, which includes links to the model.py and weights.py files. You can also find more information on the model’s performance on llm-stats.com.
It’s worth noting that the README file for GPT-oss-20b explicitly states that the PyTorch implementation is for educational purposes only and requires at least 4x H100 GPUs due to the lack of optimization.