The primary limiting factor in running large language models (LLMs) like LLaVA 1.6 34B is VRAM. This model, with 34 billion parameters, requires approximately 68GB of VRAM when using FP16 (half-precision floating point) for inference. The NVIDIA RTX 4060 Ti 16GB only provides 16GB of VRAM, resulting in a significant shortfall of 52GB. This means the model, in its native FP16 format, cannot be loaded entirely onto the GPU for processing. Attempting to run it directly will result in an out-of-memory error. The memory bandwidth of 290 GB/s on the RTX 4060 Ti, while decent, is secondary to the VRAM limitation in this scenario. Even if the model could fit, the limited bandwidth would constrain the speed at which data can be moved, impacting inference speed.
Beyond VRAM, the number of CUDA and Tensor cores also influences performance. The RTX 4060 Ti has 4352 CUDA cores and 136 Tensor cores, which are used for parallel processing and accelerating matrix multiplications, respectively. While these cores contribute to computational power, the insufficient VRAM prevents them from being fully utilized with a model of this size. Consequently, without employing substantial optimization techniques like quantization, running LLaVA 1.6 34B on an RTX 4060 Ti 16GB is not feasible for practical use.
Due to the VRAM limitations, directly running LLaVA 1.6 34B on an RTX 4060 Ti 16GB is impractical. However, you can explore techniques like quantization to reduce the model's memory footprint. Quantization involves converting the model's weights from FP16 to lower precision formats like INT8 or even INT4. This can significantly reduce the VRAM requirement, potentially bringing it within the 16GB limit. Frameworks like `llama.cpp` and `text-generation-inference` are designed to efficiently handle quantized models.
Alternatively, consider offloading some layers of the model to system RAM (CPU). This will significantly slow down inference, as data transfer between system RAM and GPU is much slower than VRAM access. Another solution is to use cloud-based inference services or platforms with GPUs that have sufficient VRAM. If local execution is a must, explore smaller models that fit within the RTX 4060 Ti's VRAM capacity or consider upgrading to a GPU with more VRAM.