The NVIDIA A100 80GB, with its substantial 80GB of HBM2e memory and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Medium 14B model, especially in its Q4_K_M (4-bit) quantized form. The model requires only 7GB of VRAM when quantized, leaving a significant 73GB of headroom on the A100. This ample VRAM allows for large batch sizes and extended context lengths, crucial for maintaining coherence and capturing long-range dependencies in text generation. The A100's 6912 CUDA cores and 432 Tensor Cores further accelerate the matrix multiplications and other computations inherent in large language model inference, contributing to high throughput. The Ampere architecture provides hardware-level optimizations for tensor operations, enhancing the efficiency of the inference process.
Given the substantial VRAM headroom, experiment with larger batch sizes to maximize throughput. A batch size of 26 is a good starting point, but you can likely increase it further without encountering memory constraints. Consider using a context length close to the model's maximum of 128000 tokens to fully leverage its capabilities for long-form content generation or complex reasoning tasks. Monitor GPU utilization and memory usage to fine-tune the batch size and context length for optimal performance. If you encounter performance bottlenecks, explore alternative quantization methods or model parallelism techniques to further optimize memory usage and computational load.