The NVIDIA H100 PCIe, with its 80GB of HBM2e VRAM, faces a significant challenge when running the Llama 3.3 70B model. Llama 3.3 70B in FP16 precision requires approximately 140GB of VRAM to load the model weights and perform inference. This means the H100 falls short by 60GB, making direct loading impossible without employing specific optimization techniques. While the H100 boasts a high memory bandwidth of 2.0 TB/s and substantial compute power with 14592 CUDA cores and 456 Tensor cores, these advantages are negated by the VRAM bottleneck.
The incompatibility stems directly from the model's memory footprint exceeding the GPU's capacity. Even though the H100 is a powerful accelerator, its 80GB VRAM limit prevents it from holding the entire model in FP16 precision. Consequently, users should not expect any usable tokens per second or batch size without employing techniques to reduce the model's VRAM usage. This limitation is a crucial consideration for anyone planning to deploy Llama 3.3 70B on an H100 PCIe, highlighting the importance of matching model size with GPU memory capacity.
Given the VRAM shortfall, running Llama 3.3 70B on a single H100 PCIe requires significant optimization. Quantization is essential; consider using 4-bit or 8-bit quantization techniques (e.g., QLoRA, bitsandbytes) to reduce the model's memory footprint. This will significantly lower the VRAM requirement, potentially bringing it within the H100's capacity. Alternatively, explore model parallelism across multiple GPUs if available, which distributes the model across several devices, mitigating the VRAM constraint on a single GPU.
If quantization proves insufficient or impacts performance unacceptably, consider using smaller models like Llama 3.3 8B or Llama 3.3 15B, which have significantly lower VRAM requirements. Another option is to offload some layers to system RAM, although this will substantially reduce inference speed. Evaluate different inference frameworks like vLLM or text-generation-inference, which offer advanced memory management and optimization techniques. Carefully monitor VRAM usage during inference to ensure the model fits within the available memory.