The NVIDIA H100 SXM, with its substantial 80GB of HBM3 VRAM, is exceptionally well-suited for running the Mistral 7B model. Mistral 7B, in FP16 precision, requires approximately 14GB of VRAM, leaving a significant 66GB of headroom. This ample VRAM allows for large batch sizes and extended context lengths without encountering memory constraints. Furthermore, the H100's impressive 3.35 TB/s memory bandwidth ensures rapid data transfer between the GPU and memory, preventing memory bottlenecks during inference. The H100's 16896 CUDA cores and 528 Tensor Cores will also contribute to extremely fast matrix multiplications and other operations critical for LLM inference.
The Hopper architecture's advanced Tensor Cores are optimized for mixed-precision computations, enabling further acceleration through techniques like FP16 or even INT8 quantization without significant accuracy loss. The large VRAM headroom facilitates experimentation with larger batch sizes, enabling higher throughput. The estimated 135 tokens/sec is an excellent starting point, but can be optimized further. The H100's high TDP of 700W indicates its power, but also ensures it has the thermal capacity to sustain peak performance during demanding workloads like LLM inference.
Given the H100's capabilities, you can maximize performance by experimenting with larger batch sizes (starting with the estimated 32) and longer context lengths, up to the model's limit of 32768 tokens. Consider using a high-performance inference framework like vLLM or NVIDIA's TensorRT to further optimize throughput. Quantization to INT8 or even INT4 (if supported by your inference framework and without unacceptable accuracy loss) can dramatically improve performance. Monitor GPU utilization and memory usage to identify any bottlenecks and fine-tune parameters accordingly. Ensure proper cooling is in place to handle the H100's 700W TDP.