The NVIDIA H100 SXM, with its massive 80GB of HBM3 memory and 3.35 TB/s bandwidth, is exceptionally well-suited for running large language models like LLaVA 1.6 7B. LLaVA 1.6 7B, in FP16 precision, requires approximately 14GB of VRAM. The H100's ample VRAM provides a substantial headroom of 66GB, allowing for large batch sizes and the potential to run multiple instances of the model concurrently, or to load larger models. The high memory bandwidth ensures that data can be transferred quickly between the GPU and memory, minimizing bottlenecks during inference. Furthermore, the H100's 16896 CUDA cores and 528 Tensor Cores are optimized for the matrix multiplications and other operations that are fundamental to deep learning, leading to high throughput and low latency.
Given the H100's capabilities, users should aim to maximize batch size to improve throughput. Experiment with different batch sizes to find the optimal balance between latency and throughput. Consider using inference frameworks like vLLM or NVIDIA's TensorRT to further optimize performance. Quantization to INT8 or even lower precision (if accuracy is acceptable) can potentially further increase throughput and reduce VRAM usage. Ensure the system has adequate cooling to handle the H100's 700W TDP.