The NVIDIA Jetson AGX Orin 32GB faces a significant challenge when attempting to run DeepSeek-Coder-V2 due to the model's substantial VRAM requirement. DeepSeek-Coder-V2, with its 236 billion parameters, necessitates approximately 472GB of VRAM in FP16 precision. The Jetson AGX Orin, equipped with only 32GB of LPDDR5 memory, falls drastically short of this requirement, resulting in a VRAM deficit of 440GB. This memory limitation will prevent the model from loading entirely onto the GPU, making direct inference impossible without substantial optimization or hardware augmentation.
Furthermore, even if aggressive quantization techniques could reduce the model's memory footprint, the Jetson AGX Orin's memory bandwidth of 0.21 TB/s poses another bottleneck. While adequate for many smaller models, this bandwidth might struggle to efficiently serve the massive parameter set of DeepSeek-Coder-V2, potentially leading to slow inference speeds. The Ampere architecture's 1792 CUDA cores and 56 Tensor Cores would be underutilized due to the memory constraints, preventing the model from achieving its potential performance. Consequently, the expected tokens per second and achievable batch size would likely be severely limited, if the model could run at all.
Given the vast difference between the model's VRAM requirements and the GPU's capacity, directly running DeepSeek-Coder-V2 on the Jetson AGX Orin 32GB is impractical. Consider exploring model quantization techniques such as 4-bit or even lower precision quantization (e.g., using bitsandbytes or similar libraries) to drastically reduce the model's VRAM footprint. Alternatively, offloading layers to system RAM might be considered, but this will severely impact performance due to the slower transfer speeds between system RAM and the GPU.
Another approach involves using a smaller, distilled version of DeepSeek-Coder-V2, if available, or exploring alternative code generation models with fewer parameters that fit within the Jetson's VRAM capacity. Finally, consider using cloud-based inference services or distributed inference across multiple GPUs to overcome the hardware limitations of a single Jetson AGX Orin.