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 Small 7B model. The Q4_K_M quantization brings the model's VRAM footprint down to a mere 3.5GB, leaving a substantial 20.5GB of headroom. This ample VRAM allows for larger batch sizes and longer context lengths without performance degradation. The RTX 4090's 16384 CUDA cores and 512 Tensor cores further accelerate the model's computations, leading to impressive inference speeds.
Given the RTX 4090's capabilities, users should experiment with larger batch sizes (up to 14 as initially estimated) and the full 128000 token context length to maximize throughput. While Q4_K_M offers a good balance of performance and VRAM usage, exploring higher quantization levels like Q5_K_M or even FP16 (if needed for ultimate quality and if you can manage the memory) could further enhance output quality, though at the expense of increased VRAM consumption. Monitor VRAM usage to ensure you don't exceed the card's capacity, especially when running other applications concurrently.