The primary limiting factor for running large language models (LLMs) like LLaVA 1.6 34B is the GPU's VRAM capacity. LLaVA 1.6 34B, with its 34 billion parameters, requires approximately 68GB of VRAM when using FP16 (half-precision floating point) for storing the model weights. The NVIDIA RTX 4060, equipped with only 8GB of VRAM, falls significantly short of this requirement. This means the entire model cannot be loaded onto the GPU at once, leading to out-of-memory errors and preventing direct inference.
While the RTX 4060 features a decent memory bandwidth of 0.27 TB/s and benefits from the Ada Lovelace architecture, including Tensor Cores for accelerated computations, these advantages are negated by the severe VRAM bottleneck. Even if techniques like offloading layers to system RAM were employed, the performance would be drastically reduced due to the slower transfer speeds between system RAM and the GPU. The limited CUDA cores (3072) compared to higher-end GPUs will also contribute to slower processing times once the VRAM issue is addressed.
Running LLaVA 1.6 34B on an RTX 4060 directly is not feasible due to the VRAM limitations. To make it work, you would need to explore aggressive quantization techniques such as 4-bit quantization (using libraries like bitsandbytes or llama.cpp) which can significantly reduce the VRAM footprint. However, even with quantization, performance will likely be limited. Consider using cloud-based GPU services or upgrading to a GPU with significantly more VRAM (e.g., RTX 3090, RTX 4090, or professional-grade GPUs) for a more practical experience. Alternatively, explore smaller models that fit within the RTX 4060's VRAM, such as LLaVA 1.5 7B or similar.