The Qwen 2.5 32B model, even when quantized to INT8, requires 32GB of VRAM. The NVIDIA RTX 3090 Ti has 24GB of VRAM, leaving a deficit of 8GB. This VRAM shortfall means the model cannot be loaded entirely onto the GPU for inference. While the RTX 3090 Ti's 1.01 TB/s memory bandwidth and substantial CUDA and Tensor core counts are generally beneficial for AI tasks, they cannot compensate for the insufficient VRAM in this specific scenario.
Without sufficient VRAM, the system will likely resort to offloading parts of the model to system RAM (CPU). This dramatically reduces performance, as data transfer between system RAM and the GPU is significantly slower than VRAM access. Consequently, inference speed will be severely impacted, potentially rendering the model unusable for real-time or interactive applications. Furthermore, the large context length of 131072 tokens exacerbates the VRAM demand, as processing longer sequences requires more memory.
Given the VRAM limitation, running the Qwen 2.5 32B model on an RTX 3090 Ti is not recommended without significant compromises. Consider using a GPU with at least 32GB of VRAM, or exploring alternative models with smaller parameter sizes that fit within the 24GB VRAM capacity. If using Qwen 2.5 32B is essential, investigate extreme quantization methods like 4-bit quantization (QLoRA or similar) combined with CPU offloading, but be aware that this will likely result in very slow inference speeds.
Another approach is to utilize a multi-GPU setup, if available, where the model is distributed across multiple GPUs, effectively increasing the total available VRAM. However, this requires specialized software and configuration and is beyond the scope of a simple workaround. Evaluate cloud-based inference services as a more practical alternative if performance is critical and upgrading hardware is not feasible.