The DeepSeek-Coder-V2 model, with its 236 billion parameters, presents a significant challenge for the NVIDIA RTX 4090 due to its substantial VRAM requirement. Running this model in FP16 (half-precision floating point) necessitates approximately 472GB of VRAM. The RTX 4090, while a powerful GPU, only offers 24GB of VRAM. This creates a massive VRAM deficit of 448GB, making it impossible to load the entire model onto the GPU for inference. The memory bandwidth of 1.01 TB/s on the RTX 4090, while high, cannot compensate for the lack of sufficient on-device memory to hold the model. Consequently, attempting to run DeepSeek-Coder-V2 directly on the RTX 4090 will result in out-of-memory errors.
Given the VRAM limitations, running DeepSeek-Coder-V2 directly on a single RTX 4090 is not feasible. Potential solutions involve model quantization, offloading layers to system RAM, or utilizing distributed inference across multiple GPUs. Quantization to lower precision formats like 4-bit or 8-bit can significantly reduce VRAM usage, although it may come with a slight performance trade-off. Alternatively, consider using cloud-based services or platforms designed for large model inference, which typically offer the necessary hardware resources. If local execution is crucial, explore distributed inference frameworks that can split the model across multiple GPUs, effectively pooling their VRAM.