The NVIDIA A100 40GB GPU, with its 40GB of HBM2e memory and 1.56 TB/s memory bandwidth, is well-suited for running the Gemma 2 27B model, particularly when using quantization. The q3_k_m quantization reduces the model's VRAM footprint to approximately 10.8GB, leaving a substantial 29.2GB of VRAM headroom. This ample VRAM allows for efficient inference and potentially larger batch sizes. The A100's 6912 CUDA cores and 432 Tensor Cores further accelerate the computations required for large language model inference. The Ampere architecture provides significant performance improvements over previous generations, contributing to faster processing speeds and lower latency.
Given the A100's capabilities and the model's size after quantization, you should experience excellent performance. Start with a batch size of 5, as initially estimated, and experiment with increasing it to maximize throughput. Monitor GPU utilization and memory consumption to find the optimal balance. Consider using techniques like speculative decoding or continuous batching if your inference framework supports them to further improve tokens/sec. Ensure your system has adequate cooling to handle the A100's 400W TDP.