The primary limiting factor in running large language models (LLMs) like LLaVA 1.6 13B is VRAM (Video RAM). LLaVA 1.6 13B, with its 13 billion parameters, requires a substantial amount of VRAM to store the model weights and activations during inference. In FP16 (half-precision floating point), the model alone requires approximately 26GB of VRAM. The NVIDIA RTX 4060, equipped with 8GB of GDDR6 VRAM, falls significantly short of this requirement. This means the model cannot be loaded entirely onto the GPU, leading to out-of-memory errors or the need for offloading techniques.
Even if techniques like CPU offloading are used, the relatively low memory bandwidth (0.27 TB/s) of the RTX 4060 becomes a bottleneck. Frequent data transfers between the system RAM and the GPU will drastically reduce inference speed. The 3072 CUDA cores and 96 Tensor cores of the RTX 4060 could offer reasonable computational power if the model could fit into VRAM. However, due to the VRAM limitation, the potential performance cannot be realized. Expect extremely slow or non-functional performance without significant optimization or model modification.
Due to the severe VRAM limitations, directly running LLaVA 1.6 13B on an RTX 4060 is impractical without substantial modifications. Model quantization is essential; consider using 4-bit quantization (Q4) via llama.cpp or similar frameworks to drastically reduce the VRAM footprint. Even with quantization, performance will likely be slow.
Alternatively, consider using cloud-based inference services or a different GPU with significantly more VRAM (at least 24GB). If running locally is a must, explore smaller models that fit within the 8GB VRAM or utilize a multi-GPU setup, although the RTX 4060 is not ideal for this. Fine-tuning a smaller model for your specific use case might provide a more practical solution.