The NVIDIA RTX 3090 Ti, with its substantial 24GB of GDDR6X VRAM, is exceptionally well-suited for running the Phi-3 Mini 3.8B model. The model, when quantized to INT8, requires only 3.8GB of VRAM, leaving a significant 20.2GB headroom. This ample VRAM allows for larger batch sizes and longer context lengths, improving throughput. Furthermore, the RTX 3090 Ti's impressive memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and memory, crucial for minimizing latency during inference. The 10752 CUDA cores and 336 Tensor Cores provide substantial computational power to accelerate the matrix multiplications and other operations inherent in transformer-based models like Phi-3 Mini.
To maximize performance, utilize an optimized inference framework such as `llama.cpp` or `vLLM`, which are designed to leverage the RTX 3090 Ti's architecture efficiently. Start with a batch size of 26 and experiment with increasing it until you observe diminishing returns in tokens/sec. Given the 3090 Ti's capabilities, you should be able to comfortably utilize the full 128000 token context length. Monitor GPU utilization and temperature to ensure optimal operation. If you encounter VRAM limitations when experimenting with larger batch sizes or longer context lengths, consider further quantization to INT4 to reduce memory footprint.