The NVIDIA A100 40GB GPU is exceptionally well-suited for running the Phi-3 Medium 14B model, particularly in its INT8 quantized form. Phi-3 Medium 14B, quantized to INT8, requires approximately 14GB of VRAM. The A100, with its 40GB of HBM2e memory, provides a substantial 26GB VRAM headroom. This surplus not only ensures the model fits comfortably within the GPU's memory but also allows for larger batch sizes, longer context lengths, and concurrent execution of other tasks or models. The A100's impressive 1.56 TB/s memory bandwidth is more than sufficient to feed data to the Tensor Cores, preventing memory bandwidth from becoming a bottleneck during inference.
For optimal performance with Phi-3 Medium 14B on the A100, leverage inference frameworks like vLLM or NVIDIA's TensorRT. Experiment with batch sizes up to the estimated value of 9 to maximize throughput. Given the ample VRAM, consider increasing the context length towards the model's maximum of 128,000 tokens if your application requires it. Monitor GPU utilization and memory usage to fine-tune these parameters. If you are not already using INT8 quantization, it is highly recommended to reduce the VRAM footprint and increase performance.