The NVIDIA H100 SXM, with its substantial 80GB of HBM3 VRAM and impressive 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Mistral 7B language model. Mistral 7B, in its Q4_K_M (4-bit quantized) GGUF format, requires a mere 3.5GB of VRAM. This leaves a massive 76.5GB of VRAM headroom, ensuring that memory constraints will not be a bottleneck. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor cores, is optimized for the matrix multiplications that are fundamental to LLM inference, further accelerating the model's performance.
Given the ample resources, the H100 can easily handle large batch sizes and extended context lengths. The high memory bandwidth ensures that data can be transferred quickly between the GPU and memory, minimizing latency during inference. The Tensor Cores provide dedicated hardware acceleration for mixed-precision computations, improving both speed and efficiency. The estimated 135 tokens/sec is a testament to the H100's capabilities in running this model. This estimate will vary depending on the specific inference framework and settings used, but the H100 provides an excellent foundation for achieving high throughput.
To maximize performance with the Mistral 7B model on the NVIDIA H100, begin by leveraging an optimized inference framework like `llama.cpp` for GGUF models, or `vLLM` or `text-generation-inference` for higher precision models if you dequantize. Start with a batch size of 32, as this will likely provide good throughput without excessive latency. Experiment with increasing the batch size to further boost throughput, but monitor latency to ensure a responsive user experience. Due to the large VRAM headroom, you can experiment with increasing the context length beyond the default 32768 tokens, if your application requires it, but be mindful of the quadratic increase in memory requirements for attention mechanisms.
Consider using mixed-precision inference (e.g., FP16 or BF16) if you dequantize the model, to further accelerate computations. However, for the Q4_K_M quantization, the improvements may be marginal. Profile the model's performance using NVIDIA Nsight Systems to identify any bottlenecks and optimize accordingly. Also, be aware of the H100's 700W TDP and ensure adequate cooling to maintain optimal performance.