The NVIDIA H100 SXM, with its substantial 80GB of HBM3 memory and a memory bandwidth of 3.35 TB/s, is exceptionally well-suited for running the Phi-3 Mini 3.8B model. The q3_k_m quantization further reduces the model's VRAM footprint to a mere 1.5GB, leaving a significant 78.5GB of VRAM headroom. This ample VRAM allows for large batch sizes and extended context lengths without encountering memory constraints. The H100's 16896 CUDA cores and 528 Tensor Cores ensure rapid computation, critical for achieving high inference speeds.
Given the H100's architecture and specifications, the Phi-3 Mini 3.8B model can leverage its Tensor Cores for accelerated matrix multiplications, a core component of transformer-based language models. The high memory bandwidth minimizes data transfer bottlenecks, ensuring that the GPU cores are continuously fed with the necessary data. This results in optimal utilization of the H100's computational resources and translates to high throughput in terms of tokens generated per second. The estimated 135 tokens/sec is a testament to this efficient utilization.
For optimal performance, leverage an inference framework like `vLLM` or `text-generation-inference` that is optimized for NVIDIA GPUs and supports TensorRT. Experiment with different batch sizes to find the sweet spot between latency and throughput; a batch size of 32 is a good starting point. Given the large VRAM headroom, consider increasing the context length to fully utilize the model's capabilities. Monitor GPU utilization to ensure that the H100 is being fully utilized and adjust parameters accordingly.
If you encounter any performance bottlenecks, profile your application to identify the source. Consider using techniques like speculative decoding or attention quantization to further improve inference speed. Given the H100's power consumption (700W TDP), ensure that your system has adequate cooling and power delivery to maintain stable performance.