The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is well-suited for running the Phi-3 Medium 14B model, especially when using INT8 quantization. Quantization reduces the model's memory footprint, bringing the VRAM requirement down to a manageable 14GB. This leaves a significant 10GB VRAM headroom, which is beneficial for handling larger batch sizes and longer context lengths without encountering out-of-memory errors. The RTX 3090's substantial memory bandwidth of 0.94 TB/s ensures rapid data transfer between the GPU and memory, crucial for maintaining high inference speeds. Furthermore, the 10496 CUDA cores and 328 Tensor Cores provide ample computational power to accelerate the matrix multiplications and other operations inherent in transformer-based models like Phi-3.
For optimal performance with Phi-3 Medium 14B on the RTX 3090, prioritize using an efficient inference framework like `llama.cpp` or `vLLM`. Experiment with different batch sizes to find a balance between throughput and latency. A batch size of 3 is a good starting point, but increasing it can significantly improve tokens/sec if your application is less sensitive to latency. Also, consider using a context length smaller than the maximum of 128000 if you don't need the full length, as shorter contexts generally lead to faster processing. Monitor GPU utilization and VRAM usage to fine-tune these parameters for your specific use case. Profile your application and consider other optimization techniques like attention mechanisms to further optimize performance.