The NVIDIA H100 SXM, with its 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is a powerful GPU designed for demanding AI workloads. However, running the Llama 3.3 70B model in FP16 precision requires approximately 140GB of VRAM. This significantly exceeds the H100's capacity, resulting in a VRAM deficit of 60GB. Consequently, a direct, out-of-the-box execution of Llama 3.3 70B on the H100 SXM is not feasible due to insufficient memory to load the entire model.
While the H100's Hopper architecture and Tensor Cores are optimized for transformer models like Llama 3.3, the VRAM limitation is a critical bottleneck. The high memory bandwidth would have otherwise facilitated rapid data transfer and processing. Without sufficient VRAM, the model cannot be loaded entirely onto the GPU, preventing efficient inference. Techniques like offloading layers to system RAM would drastically reduce performance, negating the benefits of the H100's powerful architecture. Due to the lack of VRAM headroom, we cannot estimate tokens/sec or batch size.
To run Llama 3.3 70B on the NVIDIA H100 SXM, you'll need to employ advanced optimization techniques. Quantization is crucial. Consider using a 4-bit quantization method like QLoRA or GPTQ, which can significantly reduce the VRAM footprint of the model. Another approach involves model parallelism, where the model is split across multiple GPUs, but this requires a multi-GPU setup. If neither quantization nor model parallelism is viable, consider using a smaller model variant or upgrading to a GPU with more VRAM, such as those with 192GB of HBM3e.