The DeepSeek-Coder-V2 model, with its massive 236 billion parameters, presents a significant challenge for consumer-grade GPUs like the AMD RX 7900 XTX. Running such a large language model (LLM) in FP16 (half-precision floating point) requires approximately 2 bytes per parameter, translating to a staggering 472GB of VRAM. The RX 7900 XTX, equipped with 24GB of GDDR6 memory, falls drastically short of this requirement. This VRAM deficit means the entire model cannot be loaded onto the GPU for inference, rendering direct execution impossible without significant modifications. Memory bandwidth, while substantial at 0.96 TB/s on the 7900 XTX, becomes less relevant when the model cannot fit entirely within the GPU's memory.
Given the substantial VRAM difference, directly running DeepSeek-Coder-V2 on the RX 7900 XTX is not feasible without employing advanced techniques. Consider using quantization methods like 4-bit or even 2-bit to drastically reduce the model's memory footprint. Even with aggressive quantization, offloading some layers to system RAM might be necessary, which will significantly impact performance. Alternatively, explore distributed inference solutions that split the model across multiple GPUs or utilize cloud-based inference services designed for large models. If local execution is crucial, consider smaller, more manageable models that fit within the RX 7900 XTX's VRAM capacity.