The NVIDIA RTX 3090, while a powerful GPU, falls short of the VRAM requirements for running LLaVA 1.6 34B in FP16 (half-precision). LLaVA 1.6 34B, with its 34 billion parameters, demands approximately 68GB of VRAM when operating in FP16 precision. The RTX 3090 offers 24GB of GDDR6X memory. This creates a VRAM deficit of 44GB, meaning the model cannot be loaded and executed directly on the GPU without significant modifications. The RTX 3090's memory bandwidth of 0.94 TB/s is substantial, but it cannot compensate for the insufficient VRAM. The Ampere architecture and its 10496 CUDA cores and 328 Tensor cores would otherwise provide considerable computational power for inference, but the memory constraint becomes the primary bottleneck.
Due to the VRAM limitation, running LLaVA 1.6 34B directly on the RTX 3090 in FP16 is not feasible. Several strategies can be employed to mitigate this issue. The most effective is model quantization, specifically using techniques like 4-bit or 8-bit quantization (e.g., QLoRA, bitsandbytes integration). This reduces the memory footprint of the model, potentially bringing it within the RTX 3090's 24GB VRAM capacity. Another approach involves offloading some layers of the model to system RAM, but this will significantly degrade performance due to the slower transfer speeds between system RAM and the GPU. Consider using inference frameworks optimized for low-VRAM environments like llama.cpp or exllama. If feasible, consider upgrading to a GPU with more VRAM, or using multiple GPUs in parallel if your chosen inference framework supports it.