The primary limiting factor for running LLaVA 1.6 7B on an NVIDIA RTX 4060 Ti 8GB is the VRAM. LLaVA 1.6 7B, when using FP16 (half-precision floating point), requires approximately 14GB of VRAM to load the model and perform inference. The RTX 4060 Ti 8GB only provides 8GB of VRAM, leaving a deficit of 6GB. This means the model, in its standard FP16 configuration, cannot be loaded entirely onto the GPU, leading to out-of-memory errors and preventing successful inference. While the Ada Lovelace architecture and the 4352 CUDA cores of the RTX 4060 Ti offer reasonable computational power, the insufficient VRAM becomes a bottleneck.
Furthermore, even if techniques like offloading some layers to system RAM were employed (which would severely impact performance), the memory bandwidth of 288 GB/s on the RTX 4060 Ti might become a secondary constraint. Moving data between system RAM and GPU memory introduces significant latency. Without sufficient VRAM, achieving acceptable inference speeds with LLaVA 1.6 7B on this GPU is highly unlikely. The Tensor Cores, while beneficial for accelerating matrix multiplications, cannot compensate for the fundamental VRAM limitation.
Due to the VRAM constraints, running LLaVA 1.6 7B in FP16 on the RTX 4060 Ti 8GB is not feasible without significant compromises. The most practical solution is to utilize quantization techniques. Quantization reduces the memory footprint of the model by representing the weights with fewer bits. For instance, using a 4-bit quantization (Q4) can reduce the VRAM requirement to approximately 3.5GB, making the model fit within the 8GB VRAM of the RTX 4060 Ti. However, this will come at the cost of some accuracy.
Consider using inference frameworks like `llama.cpp` or `text-generation-inference`, which offer robust quantization support and optimized kernels for NVIDIA GPUs. Experiment with different quantization levels (e.g., Q4_K_S, Q5_K_M) to find a balance between VRAM usage and performance. Additionally, reduce the context length if possible, as larger context lengths consume more VRAM. Be aware that even with quantization, performance might be slower compared to running the model on a GPU with sufficient VRAM.