The primary limiting factor for running LLaVA 1.6 13B on an AMD RX 7900 XT is the GPU's VRAM capacity. LLaVA 1.6 13B in FP16 (half-precision floating point) mode requires approximately 26GB of VRAM to load the model and perform inference. The RX 7900 XT is equipped with 20GB of GDDR6 VRAM, leaving a deficit of 6GB. This means the model, in its full FP16 form, cannot be loaded onto the GPU without encountering out-of-memory errors. While the RX 7900 XT boasts a substantial memory bandwidth of 0.8 TB/s and a capable RDNA 3 architecture, these strengths are irrelevant if the model cannot fit within the available VRAM.
Further complicating matters is the absence of dedicated Tensor Cores on the RX 7900 XT. Tensor Cores, commonly found in NVIDIA GPUs, accelerate matrix multiplications, a core operation in deep learning. While AMD GPUs can still perform these operations, they typically do so less efficiently, leading to lower inference speeds compared to NVIDIA counterparts with similar specifications but equipped with Tensor Cores. The model's context length of 4096 tokens also contributes to VRAM usage, as longer context lengths require more memory to store intermediate activations during inference. Consequently, even if the model could somehow be squeezed into the 20GB VRAM, the performance would likely be suboptimal without Tensor Core acceleration.
To run LLaVA 1.6 13B on the AMD RX 7900 XT, quantization is essential. Consider using a 4-bit or 8-bit quantization method (e.g., using llama.cpp or other compatible frameworks). Quantization reduces the memory footprint of the model, potentially bringing it within the 20GB VRAM limit. However, be aware that quantization can impact the model's accuracy, with more aggressive quantization generally leading to greater accuracy loss. Experiment with different quantization levels to find a balance between VRAM usage and performance.
Another option is to explore offloading some layers of the model to system RAM. This approach can alleviate VRAM pressure but will significantly slow down inference speed due to the slower transfer rates between GPU and system memory. If feasible, consider upgrading to a GPU with more VRAM (e.g., an NVIDIA RTX 3090, RTX 4080/4090, or an AMD Radeon Pro W7900) to avoid these compromises and achieve better performance. Ensure you use the latest AMD drivers and ROCm software for optimal performance on AMD GPUs.