The NVIDIA H100 SXM, with its substantial 80GB of HBM3 memory and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Mini 3.8B model. Phi-3 Mini, in its INT8 quantized form, requires a mere 3.8GB of VRAM. This leaves a significant VRAM headroom of 76.2GB, ensuring that VRAM capacity will not be a bottleneck. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, provides ample computational resources to efficiently process the model's operations.
The combination of high memory bandwidth and abundant compute capabilities allows the H100 to handle large batch sizes and long context lengths without significant performance degradation. The estimated 135 tokens/sec inference speed is indicative of the H100's ability to rapidly process the model. The high memory bandwidth ensures that data can be transferred between the GPU's memory and compute units quickly, minimizing latency and maximizing throughput. The H100's Tensor Cores are specifically designed to accelerate the matrix multiplications that are fundamental to deep learning models, further enhancing performance.
Given the low memory footprint of the quantized model, the H100 can easily accommodate multiple instances of Phi-3 Mini, making it suitable for serving multiple concurrent requests in a production environment. This high level of resource availability also opens opportunities for experimenting with larger batch sizes or more complex inference pipelines without encountering memory constraints.
For optimal performance, utilize an inference framework such as vLLM or NVIDIA's TensorRT. These frameworks are designed to maximize GPU utilization and minimize latency. Experiment with different batch sizes to find the sweet spot between throughput and latency for your specific application. Consider using techniques like speculative decoding to further increase the tokens/second. Since the model is already INT8 quantized, further quantization may not yield significant benefits and could potentially degrade accuracy, so stick with the current quantization level.
Monitor GPU utilization and memory usage to ensure that the H100 is being fully utilized. If the GPU is underutilized, try increasing the batch size or the number of concurrent requests. If you encounter performance bottlenecks, profile your inference pipeline to identify the specific operations that are causing the slowdown. Consider optimizing these operations using techniques like kernel fusion or custom CUDA kernels.