The primary limiting factor in running large language models (LLMs) like DeepSeek-V2.5 is video memory (VRAM). DeepSeek-V2.5, with its 236 billion parameters, requires approximately 472GB of VRAM when using FP16 (half-precision floating point) data types for storing the model weights. The NVIDIA RTX 4060 Ti 8GB, as the name suggests, only has 8GB of VRAM. This creates a massive VRAM deficit of 464GB, making it impossible to load the entire model onto the GPU for inference. The Ada Lovelace architecture of the RTX 4060 Ti includes Tensor Cores which accelerate matrix multiplication, a core operation in LLMs. However, this advantage is negated by the inability to load the model.
Due to the severe VRAM limitation, running DeepSeek-V2.5 directly on the RTX 4060 Ti 8GB is not feasible without significant compromises. Model quantization is essential. Look into techniques like 4-bit or even 3-bit quantization (using libraries like `llama.cpp` or `AutoGPTQ`) to drastically reduce the VRAM footprint. Even with aggressive quantization, expect severely degraded performance and a small batch size. Consider offloading some layers to system RAM if possible, although this will further reduce inference speed. As an alternative, explore using cloud-based inference services or more powerful GPUs with significantly more VRAM if performance is critical.