The primary limiting factor for running LLaVA 1.6 34B on an NVIDIA RTX 4090 is the VRAM. LLaVA 1.6 34B, when using FP16 (half-precision floating point) for its weights, requires approximately 68GB of VRAM to load the model and perform inference. The RTX 4090 has 24GB of VRAM, leaving a significant deficit of 44GB. This means the model, in its standard FP16 configuration, cannot be loaded onto the GPU. Memory bandwidth, while important for performance, is secondary to the initial VRAM requirement. The RTX 4090's 1.01 TB/s memory bandwidth would be beneficial if the model *could* fit, allowing for relatively fast data transfer between the GPU and its memory.
Due to the insufficient VRAM, directly running LLaVA 1.6 34B on the RTX 4090 in FP16 precision is not feasible. Without employing specific optimization techniques like quantization or offloading, the model will either fail to load or run extremely slowly due to constant swapping between system RAM and GPU VRAM. Even with aggressive quantization, the performance is likely to be significantly impacted compared to running the model on a GPU with sufficient VRAM.
To run LLaVA 1.6 34B on an RTX 4090, you must significantly reduce the model's memory footprint. Quantization is the most practical approach. Consider quantizing the model to 4-bit or even 3-bit precision using libraries like `llama.cpp` or `AutoGPTQ`. This will drastically reduce the VRAM requirement, potentially bringing it within the 4090's 24GB limit. However, expect a reduction in accuracy and potentially slower inference speeds compared to FP16.
Alternatively, explore offloading layers to system RAM. Frameworks like `Accelerate` allow you to distribute the model across the GPU and system memory. This will enable you to load the entire model, but inference speed will be significantly slower due to the slower transfer speeds between system RAM and the GPU. Finally, consider using cloud-based services or renting a GPU with more VRAM if optimal performance is crucial.