The NVIDIA H100 SXM, with its 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, requiring only 7.6GB of VRAM in FP16 precision, leaves a substantial 72.4GB of VRAM headroom. This ample VRAM allows for large batch sizes and extended context lengths, crucial for maintaining coherent and contextually relevant outputs in tasks like text generation and complex reasoning. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor cores, is designed to accelerate the matrix multiplications and other computations that form the core of transformer-based models like Phi-3 Mini.
The high memory bandwidth of the H100 is critical for rapidly transferring model weights and intermediate activations between the GPU's compute units and memory. This minimizes bottlenecks and ensures that the Tensor Cores can operate at maximum efficiency. The estimated token generation rate of 135 tokens/sec reflects the H100's ability to process data quickly. The large VRAM capacity also enables the use of larger batch sizes, which can improve throughput by amortizing the overhead of kernel launches and memory transfers across multiple input sequences. However, the optimal batch size will depend on the specific application and desired latency.
For optimal performance with Phi-3 Mini on the H100, start with a batch size of 32 and a context length of 128000 tokens. Experiment with different inference frameworks like vLLM or Text Generation Inference (TGI) to find the best balance between latency and throughput. Consider using techniques like speculative decoding to further increase the token generation rate, but be aware of potential trade-offs in accuracy. Additionally, explore quantization techniques like INT8 or even INT4 to potentially reduce VRAM usage and increase performance, although this may come at the cost of some model accuracy.
Monitor GPU utilization and memory usage during inference to identify potential bottlenecks. If you encounter memory limitations when using larger batch sizes or context lengths, consider reducing the precision to FP16 or even lower, or offloading some layers to CPU memory. Ensure your system has adequate cooling to handle the H100's 700W TDP to prevent thermal throttling and maintain consistent performance.