The NVIDIA RTX 3060 Ti, while a capable GPU, falls short of the massive VRAM requirements of the DeepSeek-V3 model. DeepSeek-V3, with its 671 billion parameters, necessitates approximately 1342GB of VRAM when running in FP16 (half-precision floating point). The RTX 3060 Ti is equipped with only 8GB of GDDR6 VRAM. This enormous discrepancy of 1334GB makes direct loading and execution of the model impossible without significant modifications. The RTX 3060 Ti's memory bandwidth of 0.45 TB/s, while decent, would also become a bottleneck if the model could somehow fit into the available VRAM, severely limiting the tokens processed per second. The Ampere architecture and the presence of Tensor Cores would provide some acceleration for compatible operations, but this is inconsequential given the fundamental VRAM limitation.
Even with aggressive quantization techniques, such as converting the model to INT4 or even lower precision, fitting the entire DeepSeek-V3 model within the RTX 3060 Ti's 8GB of VRAM is highly improbable. Memory bandwidth limitations would further constrain performance. Techniques like offloading layers to system RAM could be employed, but this introduces significant latency, making real-time or interactive applications impractical. The estimated tokens per second and batch size are essentially zero in a direct, unoptimized scenario due to the VRAM bottleneck. Practical usage requires extreme downscaling or leveraging distributed computing across multiple GPUs.
Given the limitations, running DeepSeek-V3 directly on an RTX 3060 Ti is not feasible. Instead, consider exploring smaller, more manageable models that fit within the 8GB VRAM. For example, fine-tuned models with fewer parameters or distilled versions of larger models. Alternatively, utilize cloud-based inference services that offer access to GPUs with sufficient VRAM, such as those offered by NelsaHost. If you are committed to running DeepSeek-V3, investigate distributed inference frameworks that allow you to split the model across multiple GPUs, although this requires significant technical expertise and hardware investment.
Another option is to explore extremely aggressive quantization techniques, such as using 2-bit quantization, combined with CPU offloading. However, expect a substantial performance degradation and reduced output quality. Realistically, the RTX 3060 Ti is better suited for smaller language models or tasks like image generation where 8GB VRAM is sufficient. Before attempting any complex configurations, benchmark smaller models to understand the performance characteristics of your hardware.