The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, provides ample resources for running the Phi-3 Small 7B model, which requires approximately 14GB of VRAM when using FP16 precision. This leaves a substantial 10GB headroom, ensuring comfortable operation even with larger batch sizes or increased context lengths. The 3090 Ti's memory bandwidth of 1.01 TB/s is also crucial for efficiently transferring model weights and intermediate activations during inference, minimizing potential bottlenecks. Furthermore, the Ampere architecture, with its 10752 CUDA cores and 336 Tensor Cores, allows for significant parallel processing, accelerating both the forward and backward passes during model execution.
Given the generous VRAM headroom, you can experiment with larger batch sizes (up to the estimated 7) and potentially longer context lengths to maximize throughput. Start with FP16 precision for a good balance between speed and accuracy. If you encounter memory issues at larger batch sizes, consider using quantization techniques like Q4 or Q8 to reduce the model's memory footprint. Monitoring GPU utilization and temperature is recommended, especially during prolonged inference tasks, due to the 3090 Ti's high TDP.