The NVIDIA RTX 5000 Ada, with its 32GB of GDDR6 VRAM, falls short of the 68GB VRAM required to load the LLaVA 1.6 34B model in FP16 precision. This incompatibility stems directly from the model's size (34 billion parameters) and the memory footprint associated with FP16 (half-precision floating point) representation. While the RTX 5000 Ada boasts a respectable memory bandwidth of 0.58 TB/s, this bandwidth becomes irrelevant when the model cannot fit entirely within the GPU's memory. Attempting to run the model without sufficient VRAM will result in out-of-memory errors, preventing successful inference.
Furthermore, even if techniques like offloading layers to system RAM were employed, performance would be severely degraded. The relatively slower transfer speeds between system RAM and GPU memory would create a significant bottleneck, resulting in extremely slow token generation. The 12800 CUDA cores and 400 Tensor cores of the RTX 5000 Ada cannot be fully utilized if the model's data resides predominantly outside of the GPU's dedicated VRAM. Therefore, without significant optimization, the RTX 5000 Ada is fundamentally unsuitable for running LLaVA 1.6 34B in its native FP16 format.
To run LLaVA 1.6 34B on the RTX 5000 Ada, you must reduce the model's memory footprint. The primary strategy is to utilize quantization techniques, such as 4-bit or 8-bit quantization. This drastically reduces the memory required to store the model's weights, potentially bringing it within the 32GB VRAM limit. Consider using inference frameworks like llama.cpp or vLLM, which offer excellent quantization support and optimization features.
Alternatively, consider using a smaller model variant, if available. While LLaVA 1.6 34B offers higher accuracy, a smaller model with fewer parameters will require less VRAM and may be a more practical choice for the RTX 5000 Ada. If neither quantization nor switching to a smaller model is feasible, consider using a cloud-based GPU service with more VRAM.