The NVIDIA H100 SXM, while a powerful GPU, falls short of the VRAM requirements for running DeepSeek-V2.5 directly. DeepSeek-V2.5, with its 236 billion parameters, needs approximately 472GB of VRAM when using FP16 precision. The H100 SXM provides only 80GB of HBM3 memory. This results in a significant VRAM deficit of 392GB, meaning the entire model cannot be loaded onto the GPU simultaneously for inference. Consequently, direct inference without memory optimization techniques is not feasible.
Furthermore, even if VRAM capacity were sufficient, the model's size would impact memory bandwidth utilization. While the H100's 3.35 TB/s memory bandwidth is substantial, large models can still become memory-bound, limiting the achievable tokens per second. Without optimizations like quantization or offloading, the model would likely exhibit poor performance due to constant data transfer between system RAM and the GPU. The high TDP of the H100 (700W) also suggests it's designed for maximum compute throughput, which cannot be fully leveraged in this scenario due to memory limitations.
To run DeepSeek-V2.5 on an H100 SXM, you'll need to employ advanced memory optimization techniques. Quantization is crucial; consider using 4-bit or even 3-bit quantization to drastically reduce the model's memory footprint. Model parallelism and offloading parts of the model to CPU RAM (while slower) can also help. Frameworks like vLLM or text-generation-inference are designed to handle such large models efficiently and provide features like tensor parallelism and optimized memory management. However, even with these optimizations, expect a significantly reduced inference speed compared to running the model on a GPU with sufficient VRAM.
Alternatively, explore distributed inference across multiple H100 GPUs if available. This approach would partition the model across several GPUs, effectively increasing the aggregate VRAM. Cloud-based inference services or specialized hardware solutions designed for large language model serving might be more suitable for optimal performance and scalability.