The NVIDIA H100 SXM, with its 80GB of HBM3 memory and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Mistral 7B language model. Mistral 7B, in its INT8 quantized form, requires only 7GB of VRAM, leaving a substantial 73GB of headroom. This ample VRAM allows for large batch sizes and the potential to run multiple model instances concurrently. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, provides significant computational power for both inference and fine-tuning tasks.
Given the H100's high memory bandwidth, the model's performance will primarily be limited by compute rather than memory constraints. While FP16 precision would offer potentially higher accuracy, the INT8 quantization provides a good balance between performance and accuracy. The estimated 135 tokens/sec is a strong starting point, and further optimizations can likely push this number higher. The large context length supported by Mistral 7B (32768 tokens) can be fully exploited by the H100 without memory limitations.
For optimal performance, leverage inference frameworks like vLLM or NVIDIA's TensorRT. These frameworks are designed to maximize throughput on NVIDIA GPUs. Experiment with different batch sizes to find the sweet spot between latency and throughput; a batch size of 32 is a good starting point, but larger batch sizes may be possible. Monitor GPU utilization to ensure you're fully leveraging the H100's capabilities. Consider using techniques like speculative decoding to further increase token generation speed. Also ensure you're using the latest NVIDIA drivers for optimal performance.
If you require higher precision or are fine-tuning the model, consider using FP16 or bfloat16 precision, but be mindful of the increased VRAM usage. If memory becomes a bottleneck, explore techniques like model parallelism or activation checkpointing. Remember to profile your application to identify any performance bottlenecks and address them accordingly.