The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and 1.01 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Mini 3.8B model. The Q4_K_M quantization of Phi-3 Mini significantly reduces the model's VRAM footprint to approximately 1.9GB. This leaves a substantial 22.1GB of VRAM headroom, ensuring that the RTX 4090 can easily accommodate the model and any additional overhead from the operating system or other applications. The Ada Lovelace architecture of the RTX 4090 also provides ample CUDA and Tensor cores, which are crucial for accelerating the matrix multiplications and other computations involved in running large language models.
Given the abundant VRAM and computational power of the RTX 4090, users should experiment with larger batch sizes and context lengths to maximize throughput. While the Q4_K_M quantization offers a good balance between performance and memory usage, consider experimenting with unquantized (FP16) or higher precision quantization levels (e.g., Q8_0) if you need even better output quality and have the VRAM to spare. Also, ensure that you have the latest NVIDIA drivers installed to take full advantage of the RTX 4090's capabilities.