The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM, is well-suited for running the Qwen 2.5 14B model, especially when using quantization. The provided Q4_K_M (4-bit) quantization significantly reduces the model's memory footprint to approximately 7GB. This leaves a substantial 17GB VRAM headroom, ensuring smooth operation without memory-related bottlenecks. The RTX 3090 Ti's 1.01 TB/s memory bandwidth further contributes to efficient data transfer between the GPU and memory, crucial for LLM inference.
Beyond VRAM, the RTX 3090 Ti's 10752 CUDA cores and 336 Tensor Cores are instrumental in accelerating the matrix multiplications and other computations inherent in LLM inference. While the model size is substantial, the combination of ample VRAM, high memory bandwidth, and numerous compute cores allows for reasonable inference speeds. The estimated 60 tokens/sec indicates interactive performance, suitable for many real-world applications. The Ampere architecture also contributes to performance through its optimized tensor cores and memory architecture.
Given the comfortable VRAM headroom, you can experiment with slightly larger batch sizes or longer context lengths to potentially improve throughput, although this may impact latency. Monitor VRAM usage to ensure you remain within the 24GB limit. Consider using a framework like `llama.cpp` for CPU offloading of layers if you encounter any VRAM issues, although this will reduce performance. For optimal performance, ensure you have the latest NVIDIA drivers installed and that your system has sufficient cooling to handle the RTX 3090 Ti's 450W TDP.