The DeepSeek-V2.5 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA RTX 3070 Ti due to its substantial VRAM requirements. Running DeepSeek-V2.5 in FP16 (half-precision floating point) mode necessitates approximately 472GB of VRAM to load the entire model. The RTX 3070 Ti, equipped with only 8GB of VRAM, falls drastically short of this requirement, resulting in a VRAM deficit of 464GB. This discrepancy prevents the model from being loaded onto the GPU for inference. The RTX 3070 Ti's memory bandwidth of 0.61 TB/s, while respectable, is also insufficient to compensate for the massive data transfer needs of such a large model, even if VRAM capacity were somehow addressed.
Furthermore, the limited VRAM capacity directly impacts the achievable batch size and context length. A larger model demands more memory for storing intermediate activations and gradients during processing. With only 8GB of VRAM, the RTX 3070 Ti would struggle to process even small batches or utilize the model's full 128,000 token context length. The number of CUDA and Tensor cores, 6144 and 192 respectively, become largely irrelevant as the model cannot even be loaded effectively. Consequently, the expected tokens/sec output would be negligible, rendering the model practically unusable on this GPU without significant optimization or partitioning.
Given the severe VRAM limitations, directly running DeepSeek-V2.5 on an RTX 3070 Ti is not feasible without substantial compromises. Consider using cloud-based inference services that offer access to GPUs with sufficient VRAM, such as those offered by NelsaHost. Alternatively, explore model quantization techniques like 4-bit or even lower precision to drastically reduce the VRAM footprint. However, even with aggressive quantization, the 8GB VRAM may still be a bottleneck for optimal performance.
Another approach is to explore model parallelism, where the model is split across multiple GPUs, each holding a portion of the model's parameters. However, this requires significant technical expertise and specialized software. If local execution is paramount, consider using smaller models that fit within the RTX 3070 Ti's VRAM capacity or offloading some layers to system RAM, albeit at a significant performance penalty. Finally, look into using CPU inference if no other options are available, understanding that it will be significantly slower than GPU inference.