The primary limiting factor for running large language models (LLMs) like Qwen 2.5 14B is VRAM. This model, in FP16 precision, requires approximately 28GB of VRAM to load the model weights and handle inference operations. The NVIDIA RTX 4090, while a powerful GPU, is equipped with 24GB of GDDR6X VRAM. This creates a deficit of 4GB, meaning the model cannot be loaded and run directly in FP16 without modifications. Exceeding VRAM capacity results in the system attempting to use system RAM, which is significantly slower, or outright failure. The RTX 4090's impressive memory bandwidth of 1.01 TB/s and its architecture are beneficial, but irrelevant if the model doesn't fit in VRAM.
Even with sufficient VRAM, memory bandwidth plays a crucial role in inference speed. The 4090's high memory bandwidth allows for faster data transfer between the GPU's memory and compute units, which contributes to higher tokens/second generation rate. However, in this scenario, the VRAM limitation completely overshadows the potential performance benefits of the card's other features. The number of CUDA and Tensor cores are also important for the speed of computations, but are irrelevant if the model cannot be loaded onto the GPU.
To run Qwen 2.5 14B on an RTX 4090, you'll need to significantly reduce the model's memory footprint. The most common approach is quantization, which reduces the precision of the model's weights. Consider using 8-bit (INT8) or even 4-bit (INT4) quantization. This can be achieved using libraries like `llama.cpp` or `AutoGPTQ`. Quantization will impact the model's accuracy, but the trade-off is necessary to make it fit within the 24GB VRAM limit.
Alternatively, you could explore offloading some layers of the model to system RAM. However, this will drastically reduce inference speed due to the much slower access times of system RAM compared to VRAM. If neither of these options provides satisfactory performance, consider using a cloud-based GPU with more VRAM or splitting the model across multiple GPUs using techniques like tensor parallelism.