The NVIDIA RTX 3090 Ti, while a powerful GPU with 24GB of GDDR6X VRAM and a memory bandwidth of 1.01 TB/s, falls short of the 26GB VRAM required to run the LLaVA 1.6 13B model in FP16 (half-precision floating point) without modification. LLaVA 1.6 13B, a vision model with 13 billion parameters and a context length of 4096 tokens, demands significant memory resources to store the model weights and intermediate activations during inference. The Ampere architecture of the RTX 3090 Ti, with its 10752 CUDA cores and 336 Tensor cores, is well-suited for the computational demands of large language models, but the insufficient VRAM becomes a bottleneck in this scenario. Memory bandwidth is also crucial; while 1.01 TB/s is high, it won't compensate for the fundamental lack of memory capacity. The 450W TDP indicates the card's power draw, reflecting its high-performance capabilities, but this doesn't alleviate the VRAM constraint.
To run LLaVA 1.6 13B on the RTX 3090 Ti, you'll need to employ quantization techniques to reduce the model's memory footprint. Quantization to 8-bit integers (INT8) or even 4-bit integers (INT4) can significantly decrease VRAM usage. Consider using inference frameworks like llama.cpp or vLLM, which offer optimized quantization and memory management features. Additionally, explore offloading some layers to system RAM if possible, although this will negatively impact performance. If these measures prove insufficient, consider using a GPU with more VRAM or exploring distributed inference across multiple GPUs.