The primary limiting factor when running large language models (LLMs) like DeepSeek-V2.5 is the GPU's VRAM capacity. DeepSeek-V2.5, with its 236 billion parameters, requires approximately 472GB of VRAM when using FP16 (half-precision floating point) data types. The NVIDIA RTX 4070, equipped with only 12GB of VRAM, falls far short of this requirement. This massive discrepancy (-460GB VRAM headroom) means the entire model cannot be loaded onto the GPU simultaneously. Consequently, standard inference methods will fail due to out-of-memory errors. Memory bandwidth, while important for overall performance, becomes secondary when the model size exceeds available VRAM.
Given the severe VRAM limitation, directly running DeepSeek-V2.5 on a single RTX 4070 is impractical. Several strategies can be explored to mitigate this. Firstly, consider using aggressive quantization techniques like 4-bit or even 2-bit quantization. This drastically reduces the model's memory footprint, but may come at the cost of some accuracy. Secondly, explore 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. Finally, consider using cloud-based GPU services or distributed computing setups with multiple GPUs to meet the VRAM requirements. If the model is essential for local work, consider lower parameter models that fit in your VRAM.