The DeepSeek-Coder-V2 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA RTX 4070 SUPER due to its substantial VRAM requirements. Specifically, running DeepSeek-Coder-V2 in FP16 (half-precision floating point) necessitates approximately 472GB of VRAM. The RTX 4070 SUPER, equipped with 12GB of GDDR6X memory, falls far short of this requirement, resulting in a VRAM deficit of roughly 460GB. This disparity means the entire model cannot be loaded onto the GPU for inference, leading to a compatibility failure. Memory bandwidth, while important, becomes a secondary concern when the model's size exceeds the available memory by such a large margin. Even with the RTX 4070 SUPER's 0.5 TB/s memory bandwidth, it cannot compensate for the inability to load the model.
In practical terms, attempting to run DeepSeek-Coder-V2 on the RTX 4070 SUPER without significant modifications will result in out-of-memory errors. The model's size dictates that it cannot be directly used for inference on this GPU. The number of CUDA and Tensor cores, while indicative of computational power, are irrelevant if the model cannot reside in the GPU's memory. The context length of 128000 tokens further exacerbates the memory demands, as longer contexts require more VRAM to store intermediate calculations during inference. The estimated tokens per second and batch size are both 'None' because the model cannot even be loaded.
Given the severe VRAM limitation, running DeepSeek-Coder-V2 directly on the RTX 4070 SUPER is not feasible without substantial modifications. Consider using 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 may still be limited due to the need to offload parts of the model to system RAM or disk, which introduces significant latency.
Alternatively, explore using cloud-based inference services or renting a more powerful GPU with sufficient VRAM (e.g., an NVIDIA A100 or H100 with 80GB+ VRAM). Another option is to investigate model parallelism, where the model is split across multiple GPUs. However, setting up model parallelism can be complex and requires specialized software and expertise. For local use, consider smaller, more manageable models that fit within the RTX 4070 SUPER's VRAM capacity.