The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e VRAM, is exceptionally well-suited for running the LLaVA 1.6 13B model. LLaVA 1.6 13B, requiring 26GB of VRAM in FP16 precision, leaves a significant headroom of 54GB. This ample VRAM allows for larger batch sizes, longer context lengths, and potentially the simultaneous execution of multiple model instances or other memory-intensive tasks. The H100's impressive 2.0 TB/s memory bandwidth further ensures rapid data transfer between the GPU and memory, preventing bottlenecks during inference.
Beyond VRAM, the H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, provides substantial computational power for accelerating the matrix multiplications and other linear algebra operations that are fundamental to deep learning models like LLaVA 1.6 13B. The Tensor Cores, specifically designed for accelerating mixed-precision computations, are particularly beneficial for FP16 inference, contributing to faster processing and improved energy efficiency. The estimated tokens/second rate of 93 and a batch size of 20 are indicative of the H100's capability to handle this model with high throughput.
Given the H100's capabilities, users should prioritize maximizing batch size to improve throughput and overall efficiency. Experiment with different batch sizes up to the estimated limit of 20 to find the optimal balance between latency and throughput for your specific application. Consider using an optimized inference framework like vLLM or NVIDIA's TensorRT to further accelerate inference and reduce latency. Also, monitor GPU utilization and memory usage to identify potential bottlenecks and adjust settings accordingly. If you are only using a small portion of the context window, consider reducing the context length to save on compute and memory usage.
While FP16 precision is a good starting point, explore quantization techniques like INT8 or even INT4 to potentially further reduce memory footprint and increase inference speed, albeit with a possible trade-off in accuracy. Thoroughly evaluate the accuracy impact of any quantization method before deploying it in a production environment. Utilize profiling tools to identify performance bottlenecks and optimize specific parts of the inference pipeline.