The NVIDIA RTX 3080 Ti, with its 12GB of GDDR6X VRAM, falls significantly short of the 1342GB VRAM requirement for running DeepSeek-V3 in FP16 (half-precision floating point). DeepSeek-V3's massive 671 billion parameters necessitate an enormous amount of memory to store the model weights and activations during inference. The RTX 3080 Ti's memory bandwidth of 0.91 TB/s, while substantial, becomes a bottleneck when attempting to load and process the model's data across system memory due to insufficient VRAM. This mismatch results in the incompatibility verdict, making direct inference impossible without substantial modifications.
Due to the extreme VRAM deficit, even with aggressive quantization techniques, the model's entire footprint cannot be accommodated within the RTX 3080 Ti's memory. While techniques like offloading layers to system RAM could technically allow the model to 'run', the performance would be severely degraded, rendering it impractical. The high memory bandwidth demand of the model, when coupled with the relatively slow transfer rates between VRAM and system RAM, would create an extreme bottleneck, resulting in unacceptably low tokens per second and effectively zero batch size. The Ampere architecture of the RTX 3080 Ti, while powerful, cannot overcome the fundamental memory limitation in this scenario.
Given the vast disparity in VRAM requirements, running DeepSeek-V3 on an RTX 3080 Ti for practical inference is not feasible. Instead, consider using cloud-based inference services like those offered by NelsaHost, which provide access to GPUs with significantly more VRAM, such as the A100 or H100. Alternatively, explore smaller models that can fit within the RTX 3080 Ti's VRAM, or consider distributing the model across multiple GPUs using frameworks like DeepSpeed. Model distillation to a smaller, more manageable model is another viable option, although it might sacrifice some accuracy. Finetuning on a smaller, more efficient model can often achieve comparable results for specific tasks.
If cloud-based inference or model distillation aren't options, investigate extreme quantization techniques such as 4-bit or even 2-bit quantization, though these will likely lead to a significant drop in model quality. Even with extreme quantization, successful inference is not guaranteed. Also, consider using a CPU based inference framework that can leverage system RAM, but expect extremely slow performance.