The primary limiting factor for running Llama 3.1 70B on a single NVIDIA H100 PCIe card is the VRAM. Llama 3.1 70B in FP16 precision requires approximately 140GB of VRAM to load the model weights and perform inference. The H100 PCIe, while a powerful GPU, only offers 80GB of HBM2e memory. This results in a VRAM deficit of 60GB, meaning the model cannot be loaded onto the GPU in its native FP16 format.
Even if quantization techniques are employed to reduce the memory footprint, the H100's 2.0 TB/s memory bandwidth will become a bottleneck if the model is heavily quantized (e.g., INT4). While the H100's Hopper architecture and Tensor Cores are designed for efficient matrix multiplications, the insufficient VRAM prevents leveraging these features effectively. Without sufficient VRAM, the model will either fail to load or experience extremely slow performance due to constant swapping between system RAM and GPU memory, rendering it unusable for practical applications.
Furthermore, the context length of 128000 tokens, while impressive, exacerbates the VRAM issue. Longer context lengths require more memory for storing attention keys and values during inference. Given the already constrained VRAM, utilizing the full context length is not feasible without significant compromises in model precision or batch size, further diminishing performance.
Due to the VRAM limitation, running Llama 3.1 70B on a single H100 PCIe requires aggressive quantization techniques. Consider using a quantization method like 4-bit quantization (INT4) or even lower to drastically reduce the model's memory footprint. Frameworks like `llama.cpp` or `vLLM` offer efficient quantization and inference capabilities.
Alternatively, explore distributed inference solutions. Model parallelism can distribute the model across multiple H100 GPUs, effectively increasing the aggregate VRAM. Frameworks like PyTorch's `torch.distributed` or NVIDIA's TensorRT-LLM can be used to implement model parallelism. However, this approach introduces communication overhead between GPUs, which can impact latency and throughput.