The NVIDIA H100 SXM, with its 80GB of HBM3 VRAM, provides ample memory for running the LLaVA 1.6 34B model, which requires approximately 68GB of VRAM when using FP16 precision. This leaves a comfortable 12GB headroom, allowing for efficient operation without memory constraints. The H100's substantial 3.35 TB/s memory bandwidth ensures rapid data transfer between the GPU and memory, crucial for the model's performance, especially during large batch processing. The presence of 16896 CUDA cores and 528 Tensor Cores further accelerates the computations required for the LLaVA model's vision and language processing tasks.
Given the H100's Hopper architecture and its optimized Tensor Cores, the LLaVA 1.6 34B model is expected to perform efficiently. While the estimated tokens/sec is around 90, this can vary depending on the input prompt complexity and the specific inference framework used. A batch size of 1 is suggested to maximize responsiveness, but adjustments might be possible depending on the application's latency requirements. The combination of high VRAM, memory bandwidth, and specialized cores positions the H100 as an ideal platform for running this demanding multimodal model.
For optimal performance, leverage inference frameworks like vLLM or Text Generation Inference, which are designed for high-throughput serving and efficient memory management. While FP16 precision is viable given the VRAM headroom, explore quantization techniques like INT8 or even INT4 to potentially increase throughput and reduce memory footprint further, if acceptable accuracy trade-offs can be made. Monitor GPU utilization and memory consumption during inference to fine-tune the batch size and other parameters for the best balance between latency and throughput. Consider using techniques like speculative decoding to further improve performance.
Ensure that the NVIDIA drivers are up to date to take advantage of the latest performance optimizations for the Hopper architecture. Experiment with different context lengths to see how they affect performance, as longer context lengths can increase memory usage and computation time. Regularly profile your application to identify any bottlenecks and optimize accordingly.