The Qwen 2.5 72B model, with its 72 billion parameters, requires a substantial amount of VRAM for inference. In FP16 (half-precision floating point), the model necessitates approximately 144GB of VRAM to load and operate efficiently. The NVIDIA RTX 4090, while a powerful consumer GPU, is equipped with only 24GB of VRAM. This creates a significant shortfall of 120GB, rendering the model incompatible for direct loading and inference in FP16 precision on a single RTX 4090. Memory bandwidth, although high at 1.01 TB/s on the RTX 4090, becomes a secondary concern when the model cannot even fit into the available memory. Attempting to run the model without sufficient VRAM will result in out-of-memory errors or extremely slow performance due to constant swapping between system RAM and GPU VRAM, effectively making it unusable.
Given the VRAM limitations, direct inference of the Qwen 2.5 72B model on a single RTX 4090 is not feasible without employing aggressive quantization techniques. Consider using quantization methods such as 4-bit or even 2-bit quantization to significantly reduce the model's memory footprint. Frameworks like llama.cpp excel at running quantized models, potentially enabling you to load and run the model, albeit with a reduction in accuracy. Alternatively, explore distributed inference solutions that split the model across multiple GPUs or utilize cloud-based inference services that offer the necessary resources. Fine-tuning a smaller, more manageable model might also be a viable option if the specific task allows.