The NVIDIA H100 SXM, with its 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Small 7B model. Phi-3 Small 7B, requiring only 14GB of VRAM in FP16 precision, leaves a substantial 66GB of headroom. This ample VRAM allows for large batch sizes and extended context lengths without encountering memory limitations. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, provides significant computational power for accelerating inference, particularly through Tensor Core utilization for FP16 matrix multiplications.
Given the H100's high memory bandwidth, the model's performance will likely be compute-bound rather than memory-bound. This means that optimizing the model's computational efficiency, such as through kernel fusion and efficient attention mechanisms, will be more critical than simply increasing batch size. The estimated tokens/sec of 135 is a good starting point, but can likely be improved with the right optimizations. The 128000 token context length is also fully supported by the hardware capabilities of the H100.
The H100's high TDP of 700W should also be considered. Ensure that the server or workstation hosting the GPU has adequate cooling and power delivery capabilities to maintain optimal performance and prevent thermal throttling.
For optimal performance with Phi-3 Small 7B on the H100, leverage an inference framework like 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. Start with a batch size of 32 as suggested, but increase it if latency remains acceptable. Also, monitor GPU utilization and temperature to ensure that the H100 is operating within its thermal limits.
Consider using quantization techniques like INT8 or FP8 to further reduce memory footprint and potentially increase throughput, although this may come at a slight cost in accuracy. If you encounter performance bottlenecks, profile the model's execution to identify the most computationally intensive operations and focus your optimization efforts there. Ensure you are using the latest NVIDIA drivers and CUDA toolkit for optimal performance.