The DeepSeek-Coder-V2 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA Jetson AGX Orin 64GB due to its substantial VRAM requirement. In FP16 (half-precision floating point), DeepSeek-Coder-V2 demands approximately 472GB of VRAM. The Jetson AGX Orin 64GB, equipped with 64GB of LPDDR5 memory, falls drastically short, resulting in a VRAM deficit of 408GB. This incompatibility means the entire model cannot be loaded onto the GPU for inference, precluding direct execution. The memory bandwidth of 0.21 TB/s on the Jetson AGX Orin 64GB, while respectable for its class, becomes a secondary bottleneck given the primary limitation imposed by insufficient VRAM.
Even if aggressive quantization techniques were employed, the sheer size of the model relative to the available VRAM makes direct inference impractical. The Ampere architecture of the Jetson AGX Orin, with its CUDA and Tensor cores, could potentially accelerate smaller, quantized versions of the model. However, without sufficient VRAM to hold even a significantly compressed version of DeepSeek-Coder-V2, the model's context length of 128,000 tokens becomes irrelevant. The system simply cannot load and process the model effectively.
Due to the severe VRAM limitations, running DeepSeek-Coder-V2 directly on the NVIDIA Jetson AGX Orin 64GB is not feasible. Consider exploring distributed inference solutions, where the model is sharded across multiple devices, although this approach adds significant complexity. A more practical approach involves utilizing cloud-based inference services or a more powerful local GPU with sufficient VRAM, such as an NVIDIA RTX 4090 or A100.
Alternatively, focus on smaller, more efficient code generation models that can fit within the Jetson AGX Orin's memory constraints. Models with fewer parameters and shorter context lengths will be more suitable for this hardware. Look into fine-tuning smaller models on code generation tasks to achieve reasonable performance within the device's limitations.