The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the Gemma 2 9B model, especially when using quantization. Gemma 2 9B in its full FP16 precision requires approximately 18GB of VRAM, which the RTX 3090 can comfortably handle. However, with q3_k_m quantization, the model's VRAM footprint is reduced dramatically to just 3.6GB. This leaves a substantial 20.4GB of VRAM headroom, allowing for larger batch sizes, longer context lengths, and the potential to run other applications concurrently without memory constraints. The RTX 3090's memory bandwidth of 0.94 TB/s ensures fast data transfer between the GPU and memory, further enhancing performance.
The RTX 3090's 10496 CUDA cores and 328 Tensor cores are crucial for accelerating the matrix multiplications and other computations inherent in deep learning inference. Tensor cores, in particular, are optimized for mixed-precision operations, enabling faster and more efficient computation with quantized models like the q3_k_m version of Gemma 2 9B. While the RTX 3090 has a TDP of 350W, the relatively small VRAM footprint of the quantized model should keep the GPU within reasonable thermal limits during inference, although good cooling is always recommended.
Given the ample VRAM available on the RTX 3090, users can experiment with larger batch sizes to maximize throughput. Starting with a batch size of 11 is a good baseline, but increasing it further, while monitoring VRAM usage, could lead to even better performance. Also consider experimenting with different context lengths to see how it impacts the inference speed and memory usage. While q3_k_m quantization offers excellent memory savings, consider trying q4_k_m or even higher quantization levels if you need even more memory for other tasks. If you encounter performance bottlenecks, ensure your drivers are up to date and that you're using the latest versions of your chosen inference framework.
Consider using `llama.cpp` or `text-generation-inference` to load and run the Gemma 2 9B model. These frameworks provide efficient implementations for running LLMs and have good support for quantization. Also, explore techniques like speculative decoding, which can further increase the tokens/second rate. Remember to monitor GPU temperature and power consumption, especially when pushing the limits of batch size and context length.