The NVIDIA RTX 4060, with its 8GB of GDDR6 VRAM, falls significantly short of the VRAM requirements for running DeepSeek-V2.5, a 236 billion parameter language model. DeepSeek-V2.5 in FP16 (half-precision floating point) requires approximately 472GB of VRAM just to load the model weights. This immense discrepancy of 464GB means the RTX 4060 cannot even load the model, let alone perform any meaningful inference. The RTX 4060's memory bandwidth of 0.27 TB/s, while adequate for gaming, would also become a bottleneck if the model could somehow be made to fit, as the constant swapping of model layers between system RAM and the GPU would severely limit performance.
Directly running DeepSeek-V2.5 on an RTX 4060 is infeasible due to the extreme VRAM limitations. To experiment with such large models, consider cloud-based GPU services that offer instances with sufficient VRAM (80GB+). Alternatively, explore extreme quantization techniques like 4-bit or even 2-bit quantization, combined with CPU offloading. However, even with these optimizations, performance will be significantly degraded. For local experimentation, smaller models (e.g., 7B or 13B parameter models) are much better suited for the RTX 4060.