The NVIDIA H100 PCIe, with its massive 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Mistral 7B language model. Mistral 7B, even in its full FP16 precision, only requires 14GB of VRAM. When quantized to INT8, the VRAM footprint shrinks further to approximately 7GB. This leaves a substantial 73GB of VRAM headroom, allowing for large batch sizes, longer context lengths, and potentially the concurrent deployment of multiple model instances or other AI workloads. The H100's 14592 CUDA cores and 456 Tensor Cores further contribute to its ability to efficiently process the model's computations, leading to high inference throughput.
Given the H100's ample resources, users should prioritize maximizing throughput and minimizing latency. Start with a batch size of 32 and experiment with increasing it until VRAM utilization approaches its limit. Explore techniques such as speculative decoding and continuous batching to further enhance performance. While INT8 quantization provides a good balance of performance and accuracy, consider experimenting with FP16 or BF16 precision if the application demands the highest possible accuracy, keeping in mind the increased VRAM usage. The H100's architecture also benefits from optimized kernels; using libraries like NVIDIA's Triton or cuBLAS can yield significant speedups.