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. Phi-3 Mini, in FP16 precision, requires approximately 7.6GB of VRAM. This leaves a substantial 16.4GB VRAM headroom on the RTX 4090, allowing for comfortable operation even with larger batch sizes or more complex inference pipelines. The RTX 4090's 16384 CUDA cores and 512 Tensor Cores further accelerate the matrix multiplications and other computations that form the core of neural network inference, contributing to high throughput and low latency.
Given the ample VRAM available, users can experiment with larger batch sizes to maximize throughput. Start with a batch size around 21, as estimated, and monitor VRAM usage. Consider using a framework like `vLLM` or `text-generation-inference` which are optimized for high throughput and efficient memory management. While FP16 precision provides a good balance of speed and accuracy, explore quantization options like INT8 or even INT4 to further reduce memory footprint and potentially increase inference speed, though this might come at the cost of some accuracy. For extremely long context lengths, enabling memory offloading techniques may be needed to fully utilize the 128k token context window.