The NVIDIA A100 80GB is an excellent GPU for running large language models (LLMs) like Phi-3 Mini 3.8B. Its ample 80GB of HBM2e memory, coupled with a 2.0 TB/s memory bandwidth, ensures that the model and its associated data can be loaded and processed quickly. The A100's 6912 CUDA cores and 432 Tensor Cores further accelerate the matrix multiplications and other computations that are fundamental to LLM inference. In this specific case, the q3_k_m quantization of Phi-3 Mini brings the VRAM requirement down to a mere 1.5GB, leaving a significant 78.5GB of headroom. This substantial VRAM availability allows for larger batch sizes and longer context lengths without encountering memory limitations. The Ampere architecture of the A100 is optimized for these kinds of workloads, making this a very powerful combination.
Given the substantial VRAM headroom, experiment with larger batch sizes to maximize throughput. Start with the suggested batch size of 32, and gradually increase it while monitoring GPU utilization and latency. Using a higher batch size will generally increase the tokens/sec. Additionally, explore different inference frameworks like `vLLM` or `text-generation-inference` to take advantage of advanced optimization techniques such as continuous batching and tensor parallelism, which could potentially improve the throughput even further. If you encounter performance bottlenecks, profile your application to identify the specific areas that need optimization.