The NVIDIA RTX A6000, with its 48GB of GDDR6 VRAM, falls significantly short of the VRAM requirements for running DeepSeek-Coder-V2, a 236 billion parameter language model. DeepSeek-Coder-V2 in FP16 precision requires approximately 472GB of VRAM to load the entire model. This means the A6000 lacks the necessary memory to even load the model, let alone perform inference. While the A6000 boasts a respectable 0.77 TB/s memory bandwidth and a substantial number of CUDA and Tensor cores, these specifications are rendered irrelevant when the model cannot fit into the available VRAM. Consequently, users will encounter 'out of memory' errors and will be unable to run the model without significant modifications.
Due to the substantial VRAM deficit, directly running DeepSeek-Coder-V2 on a single RTX A6000 is not feasible. The primary options are model quantization, distributed inference, or using alternative hardware. Quantization to INT8 or even lower precisions (e.g., 4-bit) can drastically reduce VRAM requirements, potentially bringing the model within the A6000's capacity, albeit with a possible reduction in accuracy. Distributed inference involves splitting the model across multiple GPUs, each holding a portion of the model's parameters. Alternatively, consider cloud-based solutions or renting instances with GPUs possessing sufficient VRAM, such as the A100 or H100.