The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the LLaVA 1.6 7B model. LLaVA 1.6 7B, a vision model, requires approximately 14GB of VRAM when running in FP16 precision. The H100's ample VRAM provides a significant headroom of 66GB, allowing for larger batch sizes, longer context lengths, and potential for running multiple model instances concurrently. Furthermore, the H100's 14592 CUDA cores and 456 Tensor Cores will accelerate the matrix multiplications and other computations inherent in the LLaVA model, leading to high throughput.
Given the H100's capabilities, users should aim to maximize batch size to fully utilize the GPU's parallel processing power. Experiment with batch sizes up to 32, monitoring GPU utilization to avoid exceeding memory limits or encountering performance bottlenecks. Consider using inference frameworks like vLLM or NVIDIA's TensorRT to further optimize performance and reduce latency. While FP16 is a good starting point, explore lower precision options like INT8 quantization for potential speed improvements, bearing in mind the possible trade-off in accuracy. Profile the application to identify any CPU bottlenecks that might limit the GPU's performance.