The DeepSeek-Coder-V2 model, with its massive 236 billion parameters, presents a significant challenge for GPU memory. Running this model in FP16 (half-precision floating point) requires approximately 472GB of VRAM to store the model weights alone. The NVIDIA A100 80GB, while a powerful GPU, only offers 80GB of VRAM. This creates a substantial VRAM deficit of 392GB, meaning the model's weights cannot fit entirely within the GPU's memory. Consequently, direct inference is impossible without employing techniques to reduce the memory footprint.
Even if techniques like quantization are used to reduce the VRAM requirement, the A100's memory bandwidth of 2.0 TB/s will become a limiting factor. While this bandwidth is substantial, loading model weights and performing computations on a model of this size will still introduce latency. Without sufficient VRAM to hold the entire model, the system would need to constantly swap data between the GPU and system memory, dramatically reducing performance. Due to the extreme VRAM limitation, estimating tokens/sec or a practical batch size for inference is not feasible without significant modifications.
Due to the large VRAM discrepancy, running DeepSeek-Coder-V2 on a single NVIDIA A100 80GB is not feasible without significant model optimization. Consider using model quantization techniques like 4-bit or even 2-bit quantization to drastically reduce the VRAM footprint. Frameworks like `llama.cpp` excel at running quantized models, but may not fully leverage the A100's hardware. Distributed inference across multiple GPUs, each with sufficient VRAM to hold a shard of the model, is another viable approach, using frameworks like PyTorch's `torch.distributed` or specialized inference servers such as NVIDIA Triton Inference Server. If neither of these options are viable, consider using a cloud-based service with access to larger GPUs or a cluster of GPUs. Be aware that extreme quantization levels might reduce the model's accuracy.