The NVIDIA Jetson AGX Orin 64GB is exceptionally well-suited for running LLaVA 1.6 7B. With 64GB of LPDDR5 VRAM, the Orin provides ample space for the model's 14GB (FP16) VRAM requirement, leaving a substantial 50GB headroom for larger batch sizes, longer context lengths, and other processes. The Orin's Ampere architecture, featuring 2048 CUDA cores and 64 Tensor Cores, facilitates efficient matrix multiplication and other computations crucial for transformer-based models like LLaVA. The memory bandwidth of 0.21 TB/s, while not the highest available, is sufficient for feeding data to the GPU cores, allowing for reasonable inference speeds.
Given the generous VRAM headroom, experiment with increasing the batch size to maximize throughput. Start with a batch size of 32, as indicated by the initial analysis, and gradually increase it until you observe diminishing returns or memory constraints. Consider using a framework like `llama.cpp` with appropriate quantization (e.g., Q4_K_M) to potentially reduce VRAM usage further and improve inference speed. Monitor the GPU utilization and temperature to ensure optimal performance and prevent thermal throttling. For real-time applications, optimizing the image processing pipeline feeding into LLaVA is crucial to minimize latency.