The NVIDIA RTX 3060 12GB, while a capable card, falls short of the VRAM requirements for running LLaVA 1.6 7B in its native FP16 precision. LLaVA 1.6 7B requires approximately 14GB of VRAM to load the model and associated data structures, while the RTX 3060 only offers 12GB. This 2GB deficit will prevent the model from loading, resulting in out-of-memory errors. The RTX 3060's Ampere architecture provides a solid foundation with its CUDA cores and Tensor Cores, but these compute capabilities are bottlenecked by the limited memory capacity in this scenario. Memory bandwidth, at 0.36 TB/s, is adequate for smaller models but becomes a limiting factor as model size and complexity increase.
Even if the model could be forced to load with insufficient VRAM (through techniques like offloading layers to system RAM, which is strongly discouraged for performance reasons), the performance would be severely degraded. The constant swapping of data between the GPU and system memory would introduce significant latency, drastically reducing the tokens/second output and rendering the model practically unusable for real-time or interactive applications. The 112 Tensor Cores would be underutilized due to the memory bottleneck.
To run LLaVA 1.6 7B effectively on an RTX 3060 12GB, you must employ quantization techniques to reduce the model's memory footprint. Quantization lowers the precision of the model's weights, allowing it to fit into the available VRAM. Consider using 8-bit quantization (INT8) or even 4-bit quantization (GPTQ or bitsandbytes) via libraries like `transformers` with `bitsandbytes` integration or `llama.cpp`. However, be aware that quantization introduces a trade-off: it reduces VRAM usage but can also slightly decrease model accuracy. Experiment with different quantization levels to find the optimal balance between performance and accuracy for your specific use case. If quantization alone is insufficient, explore techniques like CPU offloading, but be prepared for a significant performance penalty.