The DeepSeek-Coder-V2 model, with its massive 236 billion parameters, presents a significant challenge for the NVIDIA RTX 4000 Ada. The primary bottleneck is VRAM. Running DeepSeek-Coder-V2 in FP16 (half-precision floating point) requires approximately 472GB of VRAM to load the model weights. The RTX 4000 Ada only provides 20GB of VRAM. This means the model cannot even be loaded onto the GPU without employing techniques to significantly reduce its memory footprint.
Even if the model could somehow fit into the available VRAM through extreme quantization, the memory bandwidth of the RTX 4000 Ada (0.36 TB/s) would likely become a limiting factor, particularly when dealing with a 128k context length. Generating tokens and processing such a large context would require frequent memory transfers, leading to slow inference speeds. The relatively lower number of CUDA and Tensor cores compared to high-end datacenter GPUs will also contribute to reduced performance.
Due to the substantial VRAM discrepancy, directly running DeepSeek-Coder-V2 on the RTX 4000 Ada is not feasible without significant modifications. Consider using aggressive quantization techniques like 4-bit or even 2-bit quantization to drastically reduce the model's memory footprint. Explore offloading layers to system RAM, although this will severely impact performance. Alternatively, consider using cloud-based inference services or a more powerful GPU with substantially more VRAM, such as an NVIDIA A100 or H100.
If you're determined to run it locally, focus on optimizing for minimal VRAM usage, even if it means sacrificing speed. Experiment with different inference frameworks and quantization levels to find a workable balance. Be prepared for very slow inference speeds and limited batch sizes. Running a smaller model may be more practical.