The NVIDIA RTX 4000 Ada, while a capable card for many AI tasks, falls significantly short of the VRAM requirements for running DeepSeek-V3 in its native FP16 precision. DeepSeek-V3, with its 671 billion parameters, demands approximately 1342GB of VRAM when using FP16 (half-precision floating point). The RTX 4000 Ada provides only 20GB of GDDR6 VRAM. This vast discrepancy of 1322GB means the model cannot be loaded into the GPU's memory in its entirety, leading to a compatibility failure. The RTX 4000 Ada's memory bandwidth of 0.36 TB/s is also a limiting factor, even if sufficient VRAM were available, potentially causing a bottleneck during inference. The Ada Lovelace architecture provides benefits from Tensor Cores, but they cannot compensate for the sheer lack of memory.
Due to the extreme VRAM difference, running DeepSeek-V3 directly on the RTX 4000 Ada is not feasible without significant modifications. Consider using quantization techniques like 4-bit or even lower to drastically reduce the model's memory footprint. Even with aggressive quantization, the model's size might still pose challenges. Alternatively, explore cloud-based solutions or renting GPUs with substantially more VRAM (80GB+). Model parallelism across multiple GPUs is another option, but it requires significant technical expertise and infrastructure. If you have access to a CPU with large RAM, offloading some layers to the CPU might be a last resort, but inference speeds will be dramatically reduced.