The video demonstrates the RTX Pro 6000’s impressive ability to run large language models like Llama 3 with 370 billion parameters in a Windows environment, achieving around 20 tokens per second with 8-bit quantization and up to 32 tokens per second with 4-bit quantization. It highlights the hardware’s high VRAM capacity, efficient model handling, and performance improvements through lower-bit quantization, along with noting the noticeable coil whine during operation.
The video showcases the RTX Pro 6000 graphics card, highlighting its impressive hardware specifications, notably its 96 GB of VRAM. The presenter demonstrates that the card is fully operational and ready for high-performance tasks, emphasizing that the testing is conducted within a Windows environment. While acknowledging that Linux might offer marginally better performance, the focus remains on evaluating the card’s capabilities in Windows, which is a common and accessible platform for many users.
The primary test involves running a large language model (LLM), specifically Llama 3 with 370 billion parameters, using an 8-bit quantization. The presenter reports achieving a processing speed of approximately 20 tokens per second with this setup. The model’s size is also discussed, noting that it is 69 GB on disk and consumes about 72 GB of memory during operation, indicating efficient utilization of the GPU’s substantial VRAM for handling such a massive model.
Next, the presenter switches to testing a version of the model quantized to 4 bits, which reduces the model size significantly. The loading process is shown, including the unloading of the previous model and the loading of the new one into memory. Once loaded, the performance improves, with the system generating around 32 tokens per second. This demonstrates the benefits of lower-bit quantization in increasing inference speed, albeit with potential trade-offs in model accuracy.
Throughout the testing, the presenter notes the physical characteristics of the system, such as the coil whine produced during operation. The whine is described as quite noticeable, adding a sensory detail to the hardware’s performance. The testing process involves generating simple text outputs like greetings and short stories, which serve as practical benchmarks for the model’s inference speed and responsiveness.
In conclusion, the video effectively illustrates the RTX Pro 6000’s capability to handle large language models with high efficiency in a Windows environment. The results show a clear performance boost when using lower-bit quantization, making the card suitable for demanding AI workloads. The demonstration provides valuable insights into the hardware’s real-world application, balancing technical details with practical performance metrics.