The DeepSeek-Coder-V2 model, with its 236 billion parameters, presents a significant challenge for consumer-grade GPUs like the NVIDIA RTX 3090. Running such a large model in FP16 (half-precision floating point) requires approximately 472GB of VRAM. The RTX 3090, equipped with 24GB of GDDR6X memory, falls drastically short of this requirement. This means the entire model cannot be loaded onto the GPU simultaneously, leading to out-of-memory errors or the need for complex workarounds like model parallelism across multiple GPUs, which introduces significant overhead and complexity. Memory bandwidth, while substantial on the RTX 3090 (0.94 TB/s), becomes less relevant when the primary bottleneck is the sheer lack of VRAM to hold the model.
Due to the severe VRAM limitations, directly running DeepSeek-Coder-V2 on a single RTX 3090 is impractical without significant modifications. Consider using quantization techniques like 4-bit or even lower precision (e.g., using bitsandbytes library) to drastically reduce the model's memory footprint. Alternatively, explore cloud-based inference services or platforms with access to larger GPUs or multi-GPU setups designed for large language model inference. If you must run locally, investigate model parallelism frameworks, but be prepared for a significant performance hit due to inter-GPU communication overhead. Another option is to use CPU offloading, where parts of the model reside in system RAM, but this will result in very slow inference speeds.