The NVIDIA RTX 3060 Ti, with its 8GB of GDDR6 VRAM, falls significantly short of the memory requirements for running the DeepSeek-V2.5 model. DeepSeek-V2.5, a large language model (LLM) with 236 billion parameters, necessitates approximately 472GB of VRAM when using FP16 (half-precision floating point) for its weights and activations. This massive discrepancy of 464GB between required and available VRAM makes direct inference impossible without substantial modifications. The RTX 3060 Ti's memory bandwidth of 0.45 TB/s, while decent for its class, would also become a bottleneck if the model could somehow fit into the available memory, further hindering performance.
Even with aggressive quantization techniques, like converting the model's weights to INT4 or even lower precision, fitting the entire model into 8GB of VRAM is highly unlikely. The Ampere architecture of the RTX 3060 Ti provides some support for tensor cores, which accelerate matrix multiplications crucial for deep learning, but this advantage is negated by the sheer size of the model and the VRAM constraint. The model's context length of 128,000 tokens further exacerbates the memory demands, as longer contexts require proportionally more VRAM to store intermediate activations during inference.
Given the VRAM limitations, directly running DeepSeek-V2.5 on an RTX 3060 Ti is not feasible. Consider using cloud-based inference services like those offered by NelsaHost or other providers that offer access to GPUs with sufficient VRAM, such as NVIDIA A100 or H100. Alternatively, explore model parallelism, which involves splitting the model across multiple GPUs, but this requires significant technical expertise and specialized software. For local experimentation, focus on smaller models that fit within the 8GB VRAM limit or utilize CPU-based inference, although performance will be significantly slower. Another option is to use a quantized and distilled version of the model, if available, which would significantly reduce the VRAM footprint at the cost of some accuracy.