The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Mistral 7B language model, especially when employing INT8 quantization. INT8 quantization reduces the model's memory footprint to approximately 7GB, leaving a substantial 17GB VRAM headroom. This ample VRAM allows for larger batch sizes and extended context lengths without encountering memory constraints. The RTX 3090's high memory bandwidth (0.94 TB/s) further ensures efficient data transfer between the GPU and memory, minimizing potential bottlenecks during inference.
Given the RTX 3090's robust specifications, users can confidently experiment with larger batch sizes (up to 12) and the model's full context length (32768 tokens) to maximize throughput. Start with the suggested settings and monitor GPU utilization and token generation speed. If performance is satisfactory, consider increasing the batch size incrementally to further optimize throughput. If you encounter performance bottlenecks, try reducing the context length or experimenting with different inference frameworks.