The NVIDIA RTX 3080 Ti, with its 12GB of GDDR6X VRAM, falls short of the 14GB VRAM requirement for running LLaVA 1.6 7B in FP16 (half-precision floating point). This 2GB deficit means the model, in its full FP16 precision, cannot be loaded entirely onto the GPU, leading to out-of-memory errors. While the RTX 3080 Ti boasts a high memory bandwidth of 0.91 TB/s and a substantial number of CUDA and Tensor cores (10240 and 320 respectively), these specifications become secondary when the model exceeds available memory. The Ampere architecture provides strong compute capabilities, but it cannot circumvent the fundamental memory limitation.
Without sufficient VRAM, the system would likely resort to offloading parts of the model to system RAM, which is significantly slower. This would drastically reduce inference speed, making real-time or interactive applications impractical. Even if the model could technically run by swapping data between GPU and system memory, the performance would be severely degraded, potentially rendering the model unusable for most practical purposes. The estimated tokens per second and batch size are therefore currently unavailable due to this primary limitation.
To run LLaVA 1.6 7B on the RTX 3080 Ti, consider using quantization techniques to reduce the model's memory footprint. Quantization to 8-bit integers (INT8) or even lower precision like 4-bit (bitsandbytes library) can significantly decrease VRAM usage, potentially bringing it within the 12GB limit. Experiment with different quantization levels to find a balance between memory usage and acceptable performance degradation.
Alternatively, explore using a smaller model variant or a distilled version of LLaVA 1.6 if available. If these options are not feasible, consider using cloud-based GPU instances with higher VRAM capacity for running the model, or splitting the model across multiple GPUs if your setup allows. Using CPU offloading is also possible, but will severely impact performance.