The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Mistral 7B model. The Q4_K_M quantized version of Mistral 7B requires only 3.5GB of VRAM, leaving a significant 76.5GB of headroom. This ample VRAM allows for large batch sizes and extended context lengths, maximizing GPU utilization and throughput. The H100's 14592 CUDA cores and 456 Tensor Cores provide the necessary compute power for efficient inference, further boosted by the Hopper architecture's optimizations for AI workloads.
Given the H100's specifications, memory bandwidth is unlikely to be a bottleneck. The high bandwidth ensures that data can be moved quickly between the GPU and memory, preventing stalls during inference. The estimated tokens/sec rate of 117 is a direct consequence of the H100's raw compute power and memory bandwidth, combined with the relatively small size of the quantized Mistral 7B model. The large VRAM headroom allows for experimentation with larger batch sizes to potentially further increase throughput, although diminishing returns may occur beyond a certain point.
The NVIDIA H100 PCIe is an excellent choice for running Mistral 7B (Q4_K_M). Start with a batch size of 32 and a context length of 32768 tokens. Monitor GPU utilization and memory usage to fine-tune these parameters for optimal performance. Consider using inference frameworks optimized for NVIDIA GPUs, such as TensorRT or FasterTransformer, to potentially unlock further performance gains. Also, consider experimenting with different quantization methods if you observe any quality degradation with Q4_K_M, although the H100's resources should easily handle higher-precision quantization.
For production deployments, prioritize efficient data loading and pre-processing pipelines to keep the H100 fully utilized. Profile your application to identify any bottlenecks and optimize accordingly. Given the H100's significant resources, you may also consider running multiple instances of the model concurrently to maximize overall throughput, provided you have sufficient CPU resources and I/O bandwidth.