The NVIDIA RTX 4070 SUPER, equipped with 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 will prevent the model from loading and running effectively without optimizations. While the RTX 4070 SUPER boasts a memory bandwidth of 0.5 TB/s and leverages the Ada Lovelace architecture with 7168 CUDA cores and 224 Tensor cores, the insufficient VRAM is the primary bottleneck. The model's parameters, context length, and architecture are irrelevant if the model cannot be fully loaded into the GPU's memory.
Without sufficient VRAM, the system will likely resort to swapping data between the GPU and system RAM, significantly degrading performance. This swapping introduces substantial latency, rendering inference speeds impractical. Even if the model were to technically 'run' through such a workaround, the tokens per second (inference speed) and maximum batch size would be severely limited, making real-time or interactive applications unfeasible. The high TDP of 220W is also a factor to consider for thermal management, but it's secondary to the VRAM limitation in this scenario.
To run LLaVA 1.6 7B on the RTX 4070 SUPER, quantization is essential. Quantizing the model to 4-bit (Q4) or 8-bit (Q8) can significantly reduce the VRAM footprint. llama.cpp is a great framework to use as it supports quantization. Even with quantization, performance will be limited by the 12GB VRAM. Experiment with different quantization methods and context lengths to find a balance between performance and accuracy. If acceptable performance cannot be achieved, consider using a cloud-based GPU with more VRAM or explore smaller vision models that fit within the RTX 4070 SUPER's memory capacity. Using a smaller context length can also help reduce VRAM usage.