The DeepSeek-V2.5 model, with its massive 236 billion parameters, presents a significant challenge for consumer-grade GPUs like the NVIDIA RTX 4060 Ti 16GB. At FP16 precision, the model requires approximately 472GB of VRAM to load the entire model. The RTX 4060 Ti 16GB, equipped with only 16GB of VRAM, falls drastically short of this requirement. This incompatibility isn't just a matter of reduced performance; the model simply cannot be loaded onto the GPU in its entirety without employing techniques like quantization or offloading layers to system RAM. The memory bandwidth of 0.29 TB/s on the RTX 4060 Ti, while decent for gaming, will further bottleneck performance if any form of offloading is used, as data transfer between system RAM and GPU memory becomes a limiting factor.
Given the substantial VRAM disparity, running DeepSeek-V2.5 directly on the RTX 4060 Ti 16GB is not feasible without significant compromises. Consider exploring extreme quantization techniques like 4-bit or even 3-bit quantization to drastically reduce the model's memory footprint. Frameworks like `llama.cpp` are well-suited for this. Alternatively, investigate offloading some model layers to system RAM, but be aware of the performance penalty due to slower data transfer. As another option, consider using cloud-based inference services or more powerful GPUs with significantly higher VRAM capacities for optimal performance. If you have access to multiple GPUs, model parallelism might be another option, although it requires more advanced setup.