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 Qwen 2.5 72B model in FP16 (half-precision floating point) requires approximately 144GB of VRAM. This discrepancy of 64GB between the model's VRAM requirement and the GPU's available VRAM makes direct inference impossible without employing specific optimization techniques. The high memory bandwidth of the H100 would otherwise contribute to fast data transfer, but the VRAM limitation is the primary bottleneck in this scenario.
Due to the insufficient VRAM, the model cannot be fully loaded onto the H100 SXM. This prevents the model from performing any meaningful inference. Without optimization, users will encounter out-of-memory errors. Factors such as context length and batch size further exacerbate the memory demands. The 528 Tensor Cores on the H100 SXM would significantly accelerate matrix multiplications during inference, but their potential remains untapped due to the VRAM constraint.
To run Qwen 2.5 72B on an H100 SXM, you must significantly reduce the model's memory footprint. The most effective method is quantization, specifically using a lower precision format like 4-bit or 8-bit. This will drastically reduce the VRAM required, potentially bringing it within the H100's 80GB limit. Experiment with different quantization methods (e.g., QLoRA, bitsandbytes) and frameworks (e.g., llama.cpp, vLLM, text-generation-inference) to find the best balance between performance and accuracy.
Alternatively, consider using model parallelism, where the model is split across multiple GPUs. However, this requires a more complex setup and specialized software. If neither quantization nor model parallelism is feasible, consider using a GPU with more VRAM or a cloud-based inference service. Remember to monitor VRAM usage closely as you adjust these settings to ensure that you are operating within the GPU's capabilities.