The primary limiting factor for running LLaVA 1.6 13B on an NVIDIA RTX 3090 is the VRAM capacity. LLaVA 1.6 13B in FP16 precision requires approximately 26GB of VRAM to load the model weights and manage the intermediate activations during inference. The RTX 3090, while a powerful card, only offers 24GB of VRAM. This 2GB deficit prevents the model from being loaded and run directly in FP16 without modifications. The RTX 3090's memory bandwidth of 0.94 TB/s is substantial and would be beneficial if the model could fit into VRAM, allowing for fast data transfer between the GPU and memory. However, the insufficient VRAM is the bottleneck.
While the RTX 3090 boasts 10496 CUDA cores and 328 Tensor cores, which contribute to fast computation, these resources cannot be fully utilized if the model cannot be loaded. The Ampere architecture of the RTX 3090 supports various optimization techniques, but they are insufficient to overcome the VRAM limitation without quantization or other advanced techniques. The TDP of 350W indicates the power consumption, which becomes relevant when considering sustained performance and thermal management, but is not the immediate issue preventing model execution.
To run LLaVA 1.6 13B on the RTX 3090, you'll need to reduce the model's VRAM footprint. The most effective approach is to use quantization. Quantization reduces the precision of the model's weights, thereby decreasing the VRAM required. Techniques like 4-bit or 8-bit quantization can significantly lower the memory footprint.
Consider using inference frameworks like `llama.cpp` or `vLLM`, which offer efficient quantization and optimized kernels for running large language models. Experiment with different quantization levels to find a balance between VRAM usage and performance. Additionally, offloading some layers to system RAM (if available) might be an option, but this will severely impact performance due to the slower transfer speeds between system RAM and the GPU.