The NVIDIA A100 80GB is exceptionally well-suited for running the Phi-3 Medium 14B model, especially when employing INT8 quantization. Phi-3 Medium 14B, in its full FP16 precision, requires approximately 28GB of VRAM. However, with INT8 quantization, this requirement is reduced to a mere 14GB. The A100's substantial 80GB of HBM2e memory provides a significant VRAM headroom of 66GB, ensuring ample space for the model, its working memory, and potentially multiple model instances or larger batch sizes. The A100's impressive 2.0 TB/s memory bandwidth further contributes to efficient data transfer, minimizing bottlenecks during inference.
Furthermore, the A100's architecture, based on NVIDIA Ampere, boasts 6912 CUDA cores and 432 Tensor Cores, specifically designed to accelerate deep learning workloads. These Tensor Cores are particularly effective at accelerating matrix multiplications, a core operation in transformer-based models like Phi-3 Medium. This hardware acceleration, combined with the high memory bandwidth and ample VRAM, translates into excellent performance for inference tasks. Expect high throughput and low latency, making the A100 an ideal choice for deploying Phi-3 Medium in production environments.
Given the substantial VRAM headroom, consider experimenting with larger batch sizes to maximize throughput. Start with the estimated batch size of 23 and incrementally increase it until you observe diminishing returns or encounter memory limitations. Explore using the full context length of 128000 tokens to leverage the model's capabilities for longer sequences. Also, ensure you're using optimized libraries and frameworks like vLLM or NVIDIA's TensorRT to further enhance performance. If you are not using INT8, consider it for a significant performance boost with minimal impact on model accuracy.
If you encounter performance bottlenecks, profile your code to identify the specific operations causing delays. Consider using techniques like tensor parallelism or pipeline parallelism to distribute the workload across multiple GPUs if scaling beyond a single A100 is necessary. Also, ensure your data loading and preprocessing pipelines are optimized to prevent them from becoming bottlenecks.