The NVIDIA H100 SXM, with its 80GB of HBM3 memory and Hopper architecture, is exceptionally well-suited for running the FLUX.1 Schnell diffusion model. FLUX.1 Schnell, at 12 billion parameters, requires approximately 24GB of VRAM when using FP16 precision. The H100's ample 80GB VRAM provides a substantial 56GB headroom, allowing for experimentation with larger batch sizes, higher resolutions in diffusion tasks, or even running multiple model instances concurrently. The H100's impressive 3.35 TB/s memory bandwidth ensures that data can be transferred efficiently between the GPU and memory, minimizing bottlenecks during inference. The presence of 528 Tensor Cores on the H100 will further accelerate the tensor operations crucial for diffusion models, significantly improving generation speed.
To maximize performance, leverage inference frameworks optimized for NVIDIA GPUs, such as TensorRT or Triton Inference Server. Experiment with different batch sizes to find the optimal balance between latency and throughput. Start with a batch size of 23, as estimated, and adjust based on observed performance. Consider using mixed precision (FP16 or BF16) for both memory efficiency and speed. Profile your application to identify any potential bottlenecks and optimize accordingly. Using a framework like vLLM could improve throughput by optimizing memory usage and kernel fusion.