The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, falls short of the 28GB required to load the Phi-3 Medium 14B model in FP16 precision. This discrepancy of 4GB means the model, in its full FP16 format, cannot be directly loaded onto the GPU for inference. While the RTX 3090 boasts a high memory bandwidth of 0.94 TB/s and a substantial number of CUDA and Tensor cores (10496 and 328, respectively), these specifications become secondary when the model exceeds the available VRAM. Attempting to run the model without sufficient VRAM will result in errors, as the necessary weights and activations cannot be stored on the GPU. Memory bandwidth, while important for data transfer speed during inference, cannot compensate for the fundamental lack of memory capacity.
To run Phi-3 Medium 14B on an RTX 3090, quantization is essential. Consider using Q4_K_M or even lower quantization levels via llama.cpp or similar frameworks. This will significantly reduce the model's memory footprint, potentially bringing it within the RTX 3090's 24GB VRAM limit. Experiment with different quantization methods to find a balance between memory usage and acceptable performance degradation. Alternatively, investigate offloading some layers to system RAM, although this will substantially reduce inference speed. If feasible, consider upgrading to a GPU with more VRAM or distributing the model across multiple GPUs using model parallelism.