The NVIDIA H100 PCIe, with its 80GB of HBM2e memory and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Qwen 2.5 14B model, especially in its Q4_K_M (4-bit quantized) form. The quantized model requires only 7GB of VRAM, leaving a substantial 73GB of headroom. This ample VRAM allows for large batch sizes and extended context lengths, maximizing GPU utilization. The H100's 14592 CUDA cores and 456 Tensor Cores will further accelerate the model's computations, ensuring low latency and high throughput.
While the model fits comfortably within the H100's VRAM capacity, the memory bandwidth plays a crucial role in performance. The H100's 2.0 TB/s bandwidth ensures that data can be rapidly transferred between the GPU's memory and compute units, preventing bottlenecks and allowing for efficient processing of large datasets and complex operations. The estimated 78 tokens/sec is a reasonable expectation given the model size and quantization level, but this can be further optimized with appropriate software configurations and batch size adjustments.
Given the substantial VRAM headroom, experiment with increasing the batch size to further improve throughput. Start with the recommended batch size of 26 and gradually increase it until you observe diminishing returns or increased latency. Also, explore different inference frameworks such as `vLLM` or `text-generation-inference`, as these frameworks often provide optimized kernels and memory management strategies that can significantly boost performance on NVIDIA GPUs. Ensure you are using the latest NVIDIA drivers for optimal performance and compatibility.
Consider using techniques like speculative decoding or continuous batching if your application requires even higher throughput. Monitor GPU utilization to ensure that the GPU is being fully utilized. If the GPU is underutilized, increasing the batch size or enabling parallelism can help improve performance. If you need even lower latency, consider using a smaller model or further quantizing the Qwen 2.5 14B model, but be aware that this may come at the cost of accuracy.