The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the Mistral 7B language model. The model, when quantized to q3_k_m, requires only 2.8GB of VRAM, leaving a substantial 21.2GB headroom. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. Furthermore, the RTX 3090 Ti's memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and memory, crucial for maintaining high inference speeds. The 10752 CUDA cores and 336 Tensor Cores contribute significantly to accelerating the matrix multiplications and other computations inherent in LLM inference.
For optimal performance, leverage the available VRAM by experimenting with larger batch sizes to maximize GPU utilization. Start with a batch size of 15, as indicated, and gradually increase it until you observe diminishing returns in tokens/sec. Given the 3090 Ti's capabilities, explore using longer context lengths to fully utilize Mistral 7B's 32768 token window. Consider using llama.cpp for efficient CPU+GPU inference or vLLM for optimized GPU-only inference with features like PagedAttention. Monitoring GPU temperature is also advised due to the 3090 Ti's high TDP; ensure adequate cooling.