The primary limiting factor for running LLaVA 1.6 13B on an NVIDIA RTX 3080 10GB is the VRAM. LLaVA 1.6 13B, when using FP16 (half-precision floating point), requires approximately 26GB of VRAM to load the model weights and perform computations. The RTX 3080 10GB only provides 10GB of VRAM, resulting in a significant shortfall of 16GB. This VRAM deficit means the model cannot be loaded entirely onto the GPU, leading to out-of-memory errors and preventing successful inference.
While the RTX 3080's memory bandwidth of 0.76 TB/s and its 8704 CUDA cores are substantial, they become irrelevant if the model cannot be loaded. Even with its 272 Tensor Cores designed for accelerating AI tasks, the lack of sufficient VRAM bottlenecks the entire process. The Ampere architecture is capable, but the hardware limitations imposed by the 10GB VRAM capacity prevent effective utilization of the GPU's other resources. Without addressing the VRAM constraint, performance will be nonexistent, and the model will fail to run.
Unfortunately, running LLaVA 1.6 13B in FP16 on an RTX 3080 10GB is not feasible due to the VRAM limitation. To run this model, you will need to explore quantization techniques or use a different GPU with more VRAM. Consider using 4-bit or 8-bit quantization to reduce the model's memory footprint. Alternatively, offloading some layers to system RAM (CPU) is possible, but this will severely impact performance.
If you can't upgrade your GPU, explore smaller models like LLaVA 7B or consider using cloud-based GPU services that offer GPUs with sufficient VRAM, such as an NVIDIA A100 or H100. Cloud solutions provide a cost-effective way to experiment with larger models without the upfront investment of purchasing new hardware.