The NVIDIA RTX 4070 Ti, equipped with 12GB of GDDR6X VRAM, falls significantly short of the 472GB VRAM demanded by the DeepSeek-V2.5 model when using FP16 precision. This vast discrepancy arises because large language models (LLMs) like DeepSeek-V2.5 store their weights and activations in memory. FP16 (half-precision floating point) uses 2 bytes per parameter. With 236 billion parameters, this translates to 472GB of VRAM. The RTX 4070 Ti's memory bandwidth of 0.5 TB/s, while substantial, would also become a bottleneck if one were to try offloading layers to system RAM. Even if offloading were possible, the performance degradation would be severe due to the much slower bandwidth of system RAM compared to the GPU's dedicated VRAM. The Ada Lovelace architecture, with its 7680 CUDA cores and 240 Tensor cores, is capable of substantial computational throughput, but this potential is unrealizable when the model's memory footprint vastly exceeds the available VRAM.
Due to the extreme VRAM deficit, directly running DeepSeek-V2.5 on the RTX 4070 Ti without significant modifications or offloading is not feasible. Attempting to load the model would likely result in an out-of-memory error. Even with aggressive quantization techniques, achieving acceptable performance would be highly challenging. The lack of sufficient VRAM impacts not only the model's ability to load but also severely limits the maximum batch size and context length that can be processed, further hindering performance. The estimated tokens/second and batch size are therefore 'None' as practical inference is impossible in this configuration.
Given the hardware limitations, running DeepSeek-V2.5 directly on the RTX 4070 Ti is impractical. Instead, consider exploring model quantization techniques, such as 4-bit or even 2-bit quantization, to drastically reduce the model's memory footprint. However, even with aggressive quantization, the performance might be unsatisfactory. An alternative approach is to leverage cloud-based inference services or platforms that offer access to GPUs with sufficient VRAM, such as NVIDIA A100 or H100 GPUs. These platforms are designed to handle large models and provide the necessary infrastructure for efficient inference.
Another option is to explore smaller language models with fewer parameters that can fit within the RTX 4070 Ti's VRAM capacity. Consider models with similar capabilities but lower parameter counts, such as those in the 7B to 30B parameter range. These smaller models can provide reasonable performance on the RTX 4070 Ti, especially when combined with quantization and other optimization techniques. Experimenting with different inference frameworks and optimization strategies is crucial to maximize performance within the available hardware constraints.