The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is exceptionally well-suited for running the Llama 3.1 8B model, especially when quantized. The q3_k_m quantization brings the model's VRAM footprint down to a mere 3.2GB, leaving a substantial 20.8GB of headroom. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory limitations. The RTX 3090 Ti's 10752 CUDA cores and 336 Tensor Cores further contribute to efficient computation, accelerating both the forward and backward passes during inference. The Ampere architecture is also optimized for the types of matrix multiplication operations that are common in LLMs.
Given the significant VRAM headroom, experiment with increasing the batch size to maximize GPU utilization and throughput. Start with the suggested batch size of 13 and incrementally increase it until you observe diminishing returns in tokens/sec or encounter VRAM limitations. While q3_k_m quantization provides a good balance between model size and accuracy, you may also want to experiment with unquantized or lower quantization levels if absolute maximum performance is desired and you are willing to trade off some VRAM efficiency. Be sure to monitor GPU temperature and power consumption, as the RTX 3090 Ti has a high TDP of 450W.