The NVIDIA RTX 3080, equipped with 10GB of GDDR6X VRAM, faces a significant challenge when running the LLaVA 1.6 7B model. LLaVA 1.6 7B, a vision model, necessitates approximately 14GB of VRAM for FP16 (half-precision floating point) inference. This 4GB deficit between the GPU's available memory and the model's requirement directly impacts the feasibility of running the model. Insufficient VRAM leads to out-of-memory errors, preventing the model from loading and executing properly. The RTX 3080's memory bandwidth of 0.76 TB/s is substantial, but irrelevant if the model cannot fit within the available VRAM.
Even with optimizations, the fundamental constraint is the VRAM limitation. While the RTX 3080's 8704 CUDA cores and 272 Tensor Cores are capable of accelerating the computations, they remain idle if the model's data cannot be loaded onto the GPU. The Ampere architecture provides hardware-level support for FP16 operations, but this advantage is negated by the inability to accommodate the model's memory footprint. Consequently, without significant quantization or offloading, the RTX 3080 10GB cannot effectively run LLaVA 1.6 7B in its standard FP16 configuration.
To run LLaVA 1.6 7B on the RTX 3080 10GB, aggressive quantization is essential. Consider using Q4 or even lower precision quantization methods via llama.cpp or similar frameworks. This will significantly reduce the model's VRAM footprint, potentially bringing it within the 10GB limit. Alternatively, explore offloading some layers to system RAM, although this will severely impact performance due to the slower transfer speeds between system RAM and the GPU. If feasible, upgrading to a GPU with more VRAM (e.g., RTX 3090, RTX 4080, or newer) is the most straightforward solution. Cloud-based inference services also present a viable alternative, as they offer access to GPUs with sufficient VRAM without requiring a hardware upgrade.