The NVIDIA RTX 3080 12GB, based on the Ampere architecture, boasts 8960 CUDA cores, 280 Tensor cores, and a memory bandwidth of 0.91 TB/s. While these specs are impressive for many AI tasks, the primary limitation when running LLaVA 1.6 7B is the 12GB of GDDR6X VRAM. LLaVA 1.6 7B, even in FP16 (half-precision floating point), requires approximately 14GB of VRAM to load the model and manage intermediate calculations during inference. This creates a 2GB VRAM deficit, preventing the model from running directly on the GPU without adjustments.
Insufficient VRAM leads to out-of-memory errors, crashing the inference process. While the RTX 3080's substantial memory bandwidth can accelerate data transfer if offloading to system RAM is employed, this significantly degrades performance. The Ampere architecture's Tensor Cores are designed to accelerate mixed-precision computations, but their effectiveness is moot if the model cannot reside entirely within the GPU's memory. Without sufficient VRAM, batch size and context length must be severely restricted, further hindering throughput. The model's 7 billion parameters contribute to the large memory footprint, as each parameter requires storage space within the GPU memory.
Due to the VRAM limitation, directly running LLaVA 1.6 7B on the RTX 3080 12GB in FP16 is not feasible. To make it work, consider quantizing the model to a lower precision, such as 8-bit integer (INT8) or even 4-bit integer (INT4). This significantly reduces the VRAM footprint, potentially bringing it within the 12GB limit. Frameworks like `llama.cpp` and `vLLM` offer excellent quantization support and CPU offloading. If quantization isn't sufficient, explore offloading some layers to system RAM using the `--cpu-offload` flag in `llama.cpp`, but be aware of the performance penalty. As a last resort, consider using a smaller model variant if available, or distributing the model across multiple GPUs if you have access to them.