The NVIDIA RTX 4070 Ti SUPER, while a powerful card for gaming and some AI tasks, falls significantly short of the VRAM requirements for running DeepSeek-V2.5. DeepSeek-V2.5, with its 236 billion parameters, necessitates a staggering 472GB of VRAM when using FP16 precision. The RTX 4070 Ti SUPER only provides 16GB of GDDR6X VRAM. This creates a massive VRAM headroom deficit of -456GB, meaning the model cannot be loaded in its entirety onto the GPU. Memory bandwidth, while respectable at 0.67 TB/s on the 4070 Ti SUPER, becomes irrelevant when the model cannot fit within the available memory.
Attempting to run DeepSeek-V2.5 on the RTX 4070 Ti SUPER without significant modifications will result in out-of-memory errors. Even with aggressive quantization techniques, the model's footprint is likely too large to fit entirely on the card. While the RTX 4070 Ti SUPER boasts 8448 CUDA cores and 264 Tensor cores, these resources cannot be effectively utilized if the model resides primarily in system RAM due to insufficient VRAM. This will lead to extremely slow inference speeds, rendering the model practically unusable for real-time applications.
Due to the substantial VRAM difference, directly running DeepSeek-V2.5 on the RTX 4070 Ti SUPER is not feasible. Consider exploring cloud-based solutions like NelsaHost, which offer instances with sufficient VRAM (80GB+ per GPU) to handle such large models. Alternatively, investigate extreme quantization methods, such as 4-bit quantization combined with CPU offloading, but be aware that this will severely impact performance. For local execution, smaller models or fine-tuned versions of DeepSeek that are designed for lower VRAM footprints would be a more practical choice. Another option is to distribute the model across multiple GPUs using techniques like tensor parallelism, but this requires significant technical expertise and specialized software.