The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, offers ample resources for running the Phi-3 Small 7B model. The model, when quantized to Q4_K_M (4-bit), requires a mere 3.5GB of VRAM. This leaves a significant 76.5GB of VRAM headroom, enabling the potential for larger batch sizes, longer context lengths, and concurrent execution of multiple model instances or other tasks. The H100's Hopper architecture, with its 14592 CUDA cores and 456 Tensor Cores, is well-suited for the matrix multiplications and other computations inherent in large language model inference, ensuring efficient processing and high throughput.
Furthermore, the high memory bandwidth of the H100 is crucial for rapidly transferring model weights and intermediate activations between the GPU's compute units and memory. This minimizes bottlenecks and maximizes the utilization of the GPU's compute resources. The combination of abundant VRAM, high memory bandwidth, and powerful compute capabilities makes the H100 an excellent choice for deploying and serving Phi-3 Small 7B, allowing for low latency and high throughput inference.
Given the abundant VRAM available, experiment with increasing the batch size to further improve throughput. Start with a batch size of 32, as initially estimated, and gradually increase it while monitoring GPU utilization and latency. Also, consider using a context length close to the model's maximum of 128000 tokens to leverage the model's full capabilities. For optimal performance, utilize inference frameworks like `vLLM` or `text-generation-inference`, which are designed for efficient inference on NVIDIA GPUs and offer features like continuous batching and optimized kernel implementations. Monitor GPU temperature and power consumption to ensure stable operation within the H100's TDP limits.
If you encounter memory-related issues despite the large VRAM headroom, double-check that other processes are not consuming excessive GPU memory. Consider offloading less critical tasks to the CPU or using a separate GPU if available. While Q4_K_M provides a good balance of performance and memory footprint, you might explore slightly higher quantization levels (e.g., Q5_K_M or Q6_K_M) if memory usage is not a constraint, potentially leading to improved accuracy at the cost of slightly increased VRAM consumption.