The NVIDIA Jetson AGX Orin 64GB is fundamentally incompatible with the DeepSeek-V3 model due to a massive VRAM deficit. DeepSeek-V3, with its 671 billion parameters, requires approximately 1342GB of VRAM when using FP16 precision. The Jetson AGX Orin, equipped with only 64GB of LPDDR5 memory, falls short by a staggering 1278GB. This discrepancy means the entire model cannot be loaded onto the GPU simultaneously, preventing direct inference. The Orin's 2048 CUDA cores and 64 Tensor cores are rendered largely irrelevant in this scenario, as they cannot operate on a model exceeding available memory. Furthermore, even if aggressive quantization techniques were employed, the model's sheer size poses significant challenges. The memory bandwidth of 0.21 TB/s, while decent for the Orin's intended applications, would likely become a bottleneck if workarounds were attempted, leading to extremely slow inference speeds.
Given the severe VRAM limitations, running DeepSeek-V3 directly on the Jetson AGX Orin 64GB is not feasible. Instead, consider these alternatives: 1) **Model Distillation:** Train a smaller, more manageable model that approximates the behavior of DeepSeek-V3. This would significantly reduce the VRAM footprint. 2) **Offloading:** Utilize a cloud-based inference service or a more powerful local server with sufficient VRAM to handle the model. The Jetson AGX Orin could then act as a client, sending requests and receiving responses. 3) **Model Splitting (impractical):** While theoretically possible to split the model across multiple devices, the communication overhead and complexity make this impractical for the Jetson AGX Orin in this scenario.