The NVIDIA Jetson AGX Orin 32GB boasts an Ampere architecture, 1792 CUDA cores, 56 Tensor cores, and 32GB of LPDDR5 VRAM with a memory bandwidth of 210 GB/s. LLaVA 1.6 7B, a vision model with 7 billion parameters, requires approximately 14GB of VRAM when using FP16 precision. The Jetson AGX Orin's ample 32GB VRAM comfortably accommodates LLaVA 1.6 7B, leaving a significant headroom of 18GB for larger batch sizes or other concurrent processes.
While VRAM is sufficient, the memory bandwidth of 210 GB/s is a crucial factor for performance. The model's performance will be influenced by how efficiently the data can be moved between memory and the GPU's processing units. The 56 Tensor cores will accelerate the matrix multiplications inherent in deep learning, leading to faster inference. Given these specifications, we estimate the Jetson AGX Orin can achieve around 90 tokens per second with a batch size of approximately 12.
For optimal performance with LLaVA 1.6 7B on the Jetson AGX Orin, it is recommended to use a framework optimized for NVIDIA GPUs, such as TensorRT or Triton Inference Server. Experimenting with lower precision formats like INT8 or even INT4 quantization can significantly reduce VRAM usage and potentially increase inference speed, though this may come at the cost of slightly reduced accuracy. Monitor GPU utilization and memory usage during inference to fine-tune batch size and other parameters for the best balance between speed and resource consumption.
If you encounter performance bottlenecks, consider offloading certain tasks, such as image preprocessing, to the CPU to reduce the load on the GPU. Also, ensure you are using the latest NVIDIA drivers and libraries to take advantage of any performance improvements or bug fixes. For production environments, explore tools like NVIDIA DeepStream for efficient video analytics pipelines.