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 Medium 14B model, especially when using quantization. Phi-3 Medium 14B in its full FP16 precision would require 28GB of VRAM, exceeding the 4090's capacity. However, with q3_k_m quantization, the model's memory footprint is reduced to approximately 5.6GB. This leaves a substantial 18.4GB of VRAM headroom, ensuring that the model and its associated processes can operate comfortably without encountering memory constraints. The RTX 4090's 16384 CUDA cores and 512 Tensor Cores further accelerate the computations required for inference, resulting in high throughput.
For optimal performance, leverage the RTX 4090's Tensor Cores by using an inference framework that supports them, such as `llama.cpp` with CUDA or TensorRT, or `vLLM`. Experiment with different batch sizes to maximize throughput without exceeding the GPU's memory or causing latency issues. While the q3_k_m quantization allows the model to fit comfortably within the VRAM, explore slightly higher quantization levels if you need even more headroom for larger batch sizes or longer context lengths. Always monitor GPU utilization and temperature to ensure stable operation, especially during extended inference sessions.