The NVIDIA H100 SXM, with its substantial 80GB of HBM3 VRAM and impressive 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Small 7B model. The model, quantized to q3_k_m, requires a mere 2.8GB of VRAM, leaving a significant 77.2GB of headroom. This ample VRAM allows for large batch sizes and extended context lengths without encountering memory constraints. Furthermore, the H100's 16896 CUDA cores and 528 Tensor Cores will enable efficient parallel processing, significantly accelerating inference speeds. The Hopper architecture's advanced features, such as the Transformer Engine, are designed to optimize performance for large language models like Phi-3.
Given the H100's capabilities and the model's relatively small footprint, focus on maximizing throughput by increasing the batch size. Start with a batch size of 32 and experiment with larger values to find the optimal balance between latency and throughput for your specific application. Consider using a high-performance inference framework like vLLM or NVIDIA's TensorRT to further optimize performance. Monitor GPU utilization and memory consumption to ensure efficient resource allocation. For production deployments, explore techniques like model parallelism if you intend to run multiple instances of the model concurrently.