The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Mistral 7B language model, especially when using quantization. The q3_k_m quantization significantly reduces the model's memory footprint to a mere 2.8GB, leaving a substantial 21.2GB of VRAM headroom. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. The RTX 3090's memory bandwidth of 0.94 TB/s ensures rapid data transfer between the GPU and memory, crucial for minimizing latency during inference. Furthermore, the 10496 CUDA cores and 328 Tensor cores provide the computational power necessary for accelerating matrix multiplications and other operations inherent in large language model inference.
Given the significant VRAM headroom, experiment with increasing the batch size to improve throughput, potentially up to the estimated limit of 15. Utilize a framework like `llama.cpp` or `vLLM` to leverage efficient quantization and optimized kernels for the RTX 3090. Consider using a context length close to the model's maximum of 32768 tokens to fully exploit its capabilities, but monitor performance as longer context lengths can impact speed. If you need even higher performance, explore techniques like speculative decoding or model parallelism across multiple GPUs, although the latter is likely unnecessary for this setup.