The NVIDIA H100 SXM, with its massive 80GB of HBM3 VRAM and impressive 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Mini 3.8B model. Phi-3 Mini, in its Q4_K_M (4-bit) quantized form, requires a mere 1.9GB of VRAM. This leaves an enormous 78.1GB of VRAM headroom, ensuring that VRAM limitations will not be a bottleneck. The H100's 16896 CUDA cores and 528 Tensor Cores further accelerate the model's computations, leading to high throughput and low latency. The high memory bandwidth is crucial for rapidly transferring model weights and activations between the GPU and memory, maximizing performance.
Given the substantial VRAM headroom, experiment with larger batch sizes to further increase throughput. Start with the suggested batch size of 32 and gradually increase it until you observe diminishing returns or encounter memory limitations. Consider using techniques like speculative decoding to boost token generation speed. Explore different inference frameworks such as `vLLM` or `text-generation-inference` to leverage optimized kernels and scheduling algorithms for the H100 architecture. Monitor GPU utilization to ensure you're maximizing the H100's capabilities.