The DeepSeek-Coder-V2 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA H100 PCIe due to its substantial VRAM requirements. Running this model in FP16 (half-precision floating point) necessitates approximately 472GB of VRAM. The H100 PCIe, while a powerful GPU, is equipped with only 80GB of HBM2e memory. This creates a VRAM deficit of 392GB, meaning the entire model cannot be loaded onto the GPU for inference. Consequently, direct inference without employing specific techniques to reduce memory footprint is impossible.
Beyond VRAM, memory bandwidth plays a crucial role in model performance. The H100's 2.0 TB/s memory bandwidth is substantial, but it becomes less relevant when the model cannot fit entirely within the GPU's memory. Even if techniques like offloading layers to system RAM are used, the transfer speed between system RAM and GPU memory will become a significant bottleneck, drastically reducing the tokens/second throughput. The large context length of 128,000 tokens further exacerbates the memory pressure, as the attention mechanism requires significant memory to store intermediate results.
Given the severe VRAM limitation, direct inference of DeepSeek-Coder-V2 on a single NVIDIA H100 PCIe is not feasible without employing advanced techniques. Consider model quantization to reduce the memory footprint. Quantization to 4-bit (bitsandbytes or GPTQ) or 8-bit (INT8) could significantly decrease the VRAM requirement, potentially bringing it closer to the H100's capacity. However, even with quantization, offloading some layers to system RAM might still be necessary, which will impact performance.
Alternatively, explore distributed inference using multiple GPUs. Frameworks like DeepSpeed or Megatron-LM allow you to split the model across multiple GPUs, effectively increasing the available VRAM. If neither quantization nor distributed inference is viable, consider using a smaller model or a cloud-based solution that offers GPUs with sufficient VRAM, such as A100 80GB or H100 94GB.