The primary limiting factor for running DeepSeek-Coder-V2 (236B parameters) on an NVIDIA H100 SXM is the VRAM capacity. DeepSeek-Coder-V2 in FP16 (half-precision floating point) requires approximately 472GB of VRAM to load the entire model. The H100 SXM provides only 80GB of HBM3 memory. This results in a significant VRAM deficit of 392GB, meaning the model cannot be loaded onto the GPU in its entirety. While the H100's impressive memory bandwidth (3.35 TB/s) and computational power (16896 CUDA cores, 528 Tensor cores) are beneficial for LLM inference, they cannot compensate for the insufficient VRAM. Without sufficient memory, the model cannot be processed, leading to a non-functional setup.
Even if techniques like offloading some layers to system RAM were employed, performance would be severely degraded due to the significantly slower transfer speeds between system RAM and the GPU compared to HBM3. The H100's Hopper architecture is designed to accelerate large language models, but this potential cannot be realized without the necessary memory footprint to house the model. The expected performance would be negligibly slow, rendering the setup impractical for real-world applications. Techniques like quantization can reduce the VRAM footprint, but even aggressive quantization may not bring the model within the H100's VRAM capacity without significant performance trade-offs.
Due to the substantial VRAM shortfall, running DeepSeek-Coder-V2 on a single H100 SXM is not feasible without significant modifications. Consider using model quantization techniques, such as 4-bit or 8-bit quantization, to reduce the VRAM footprint. However, even with quantization, the performance might be significantly impacted. Another option is to explore distributed inference across multiple GPUs with sufficient combined VRAM, using frameworks designed for model parallelism, or consider using a smaller model.
Alternatively, consider using cloud-based inference services that provide access to GPUs with larger VRAM capacities or exploring alternative models with smaller parameter counts that fit within the H100's memory limitations. For local inference, investigate using CPU offloading in conjunction with quantization, but be aware of the substantial performance penalty. Finally, if possible, consider upgrading to a GPU with more VRAM.