The primary bottleneck in running Qwen 2.5 32B on an RTX 4090 is the VRAM limitation. Qwen 2.5 32B, even with INT8 quantization, requires 32GB of VRAM. The RTX 4090, while a powerful card, only provides 24GB. This 8GB deficit prevents the model from loading entirely onto the GPU, leading to a 'FAIL' verdict. Memory bandwidth, while substantial at 1.01 TB/s, becomes irrelevant when the model cannot fully reside in the GPU's memory. The Ada Lovelace architecture and the presence of Tensor Cores would normally contribute to faster inference, but this potential is unrealized due to the VRAM constraint. Without sufficient VRAM, the system would either refuse to load the model, or attempt to use system RAM (which is significantly slower), resulting in extremely poor performance.
Even though the model is quantized to INT8, the 32GB requirement remains a hurdle. The number of CUDA and Tensor cores available on the RTX 4090 would allow for potentially impressive inference speeds if the model could fit. The TDP of 450W is also not a limiting factor in this scenario, as the power draw is within the card's capabilities. The large context length of 131072 tokens further exacerbates the VRAM demand, as larger contexts require more memory to store intermediate calculations during inference. Ultimately, the limiting factor is the inability to load the model's weights and necessary data structures onto the GPU simultaneously.
Unfortunately, running Qwen 2.5 32B with INT8 quantization directly on an RTX 4090 is not feasible due to VRAM limitations. Consider using a lower-parameter model that fits within the 24GB VRAM, such as a 13B or 7B variant of Qwen. Alternatively, investigate offloading layers to system RAM. While this allows the model to run, performance will be significantly degraded due to the slower transfer speeds between system RAM and the GPU. Another option is to use multiple GPUs, if available, and split the model across them using frameworks designed for distributed inference.
If sticking with the RTX 4090 is a priority, focus on extreme quantization techniques like 4-bit quantization (Q4) using libraries such as `llama.cpp`. However, be aware that aggressive quantization can impact model accuracy. Experiment with different quantization methods and calibration datasets to find a balance between VRAM usage and performance. Before resorting to offloading or multi-GPU setups, thoroughly explore the most aggressive quantization possible to maximize performance within the available VRAM.