The NVIDIA Jetson AGX Orin 32GB faces a significant challenge when running the DeepSeek-V2.5 model due to the model's substantial memory footprint. DeepSeek-V2.5, with its 236 billion parameters, requires approximately 472GB of VRAM when using FP16 precision. The Jetson AGX Orin, equipped with only 32GB of VRAM, falls drastically short, resulting in a VRAM deficit of 440GB. This massive discrepancy means the entire model cannot be loaded onto the GPU at once, preventing direct inference. The Jetson AGX Orin's 210 GB/s memory bandwidth, while respectable for its class, becomes a bottleneck when attempting techniques like offloading layers to system RAM, as the data transfer speeds are insufficient to maintain acceptable performance.
Furthermore, the Jetson AGX Orin's Ampere architecture, featuring 1792 CUDA cores and 56 Tensor cores, is designed for AI acceleration. However, the sheer size of DeepSeek-V2.5 overwhelms even these capabilities when the model cannot reside entirely in VRAM. Techniques like quantization and offloading might enable the model to *run*, but the resulting performance is expected to be extremely slow, potentially rendering it impractical for real-time or interactive applications. The estimated tokens per second and batch size are therefore indeterminate without significant optimization efforts.
Given the VRAM limitations, running DeepSeek-V2.5 directly on the Jetson AGX Orin 32GB is not feasible without substantial modifications. Consider exploring quantization techniques such as 4-bit or even lower precision to drastically reduce the model's memory footprint. Frameworks like `llama.cpp` are optimized for CPU and low-VRAM environments and should be investigated. Model offloading, where some layers are processed on the CPU, can be attempted, but expect a significant performance penalty due to the limited memory bandwidth.
Alternatively, consider using a smaller language model that fits within the Jetson's VRAM or explore cloud-based inference options where the model resides on a more powerful server. If local execution is a strict requirement, investigate techniques like model distillation to create a smaller, more manageable version of the DeepSeek-V2.5 model that can run efficiently on the Jetson AGX Orin.