The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e memory and 2.0 TB/s memory bandwidth, offers ample resources for running the Llama 3 8B model, especially when quantized. The q3_k_m quantization reduces the model's VRAM footprint to a mere 3.2GB, leaving a significant 76.8GB of VRAM headroom. This abundant memory capacity allows for large batch sizes and extended context lengths without encountering memory limitations. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, provides the computational power needed for efficient inference, enabling high throughput and low latency.
Given the H100's high memory bandwidth and computational capabilities, the primary performance bottleneck is unlikely to be the GPU itself. Instead, factors such as the efficiency of the inference framework, the level of optimization applied, and the data transfer rates between the CPU and GPU will play a more significant role in determining the overall inference speed. The estimated tokens/sec of 93 is a reasonable expectation, but can be increased with framework optimization and careful tuning of batch size and context length.
For optimal performance, leverage inference frameworks like `llama.cpp` with appropriate hardware acceleration or specialized solutions like `vLLM` which 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 values might be possible given the available VRAM. Ensure that the data pipeline is optimized to minimize CPU overhead and data transfer bottlenecks. Profile your application to identify specific areas for improvement, such as kernel launch overhead or memory copy times.
Consider using techniques like speculative decoding to further boost tokens/sec. Monitor GPU utilization and memory usage to ensure that the H100 is being fully utilized. If performance is still not satisfactory, explore more aggressive quantization methods or model distillation techniques to reduce the model size and computational requirements.