The NVIDIA A100 80GB is exceptionally well-suited for running the Phi-3 Small 7B model, especially when quantized to INT8. Phi-3 Small 7B in INT8 precision requires approximately 7.0GB of VRAM, leaving a substantial 73.0GB headroom on the A100. This ample VRAM allows for large batch sizes and extended context lengths, maximizing GPU utilization. The A100's 2.0 TB/s memory bandwidth ensures that data can be transferred quickly between the GPU and memory, preventing bottlenecks during inference. The 6912 CUDA cores and 432 Tensor Cores on the A100 provide significant computational power, enabling fast matrix multiplications crucial for LLM inference.
The Ampere architecture of the A100 is optimized for AI workloads, further enhancing performance. The Tensor Cores are specifically designed to accelerate mixed-precision calculations, which are commonly used in quantized models like INT8 Phi-3 Small. The large VRAM capacity also facilitates experimentation with larger models or fine-tuning tasks without memory constraints. This combination of high memory bandwidth, abundant VRAM, and powerful compute capabilities makes the A100 an ideal platform for deploying and experimenting with LLMs like Phi-3 Small.
Given the substantial VRAM headroom, experiment with larger batch sizes to maximize throughput. Start with a batch size of 32 and incrementally increase it until you observe diminishing returns or encounter memory limitations. Utilize inference frameworks optimized for NVIDIA GPUs, such as vLLM or TensorRT, to further improve performance. Consider profiling the application to identify potential bottlenecks and optimize accordingly. While INT8 quantization provides a good balance between performance and accuracy, explore FP16 or BF16 precision for applications where higher accuracy is paramount, keeping in mind the increased VRAM requirements.