The primary limiting factor in running DeepSeek-V3 (671B parameters) on an NVIDIA RTX 4060 Ti 16GB is the enormous VRAM requirement of the model. DeepSeek-V3, in FP16 precision, necessitates approximately 1342GB of VRAM. The RTX 4060 Ti 16GB only offers 16GB of VRAM, resulting in a substantial shortfall of 1326GB. This discrepancy makes it impossible to load the entire model into the GPU's memory for inference without employing significant offloading or extreme quantization techniques. Memory bandwidth, while important, becomes secondary when the model's size far exceeds available memory. The RTX 4060 Ti's 290 GB/s memory bandwidth would be a bottleneck for a model that could fit, but in this case, it's irrelevant due to the VRAM limitation.
Even with aggressive quantization, such as 4-bit or even 2-bit, the model's footprint will likely remain far too large for the RTX 4060 Ti's VRAM. Techniques like CPU offloading could be employed, but this would drastically reduce inference speed, making it impractical for most applications. The limited number of CUDA cores (4352) and Tensor Cores (136) on the RTX 4060 Ti further compound the performance challenges, even if the VRAM issue could be mitigated. The Ada Lovelace architecture provides some performance benefits, but they are insufficient to overcome the fundamental VRAM constraint.
Due to the massive VRAM requirements of DeepSeek-V3, running it directly on an RTX 4060 Ti 16GB is not feasible without severely compromising performance. Consider using smaller models with fewer parameters that fit within the GPU's VRAM. Alternatively, explore cloud-based solutions or services that offer access to GPUs with significantly more VRAM, such as those offered by NelsaHost. If you are determined to run DeepSeek-V3 locally, investigate techniques like CPU offloading, but be prepared for extremely slow inference speeds.
Another option is to use model distillation to create a smaller, more manageable model that approximates the behavior of DeepSeek-V3. This would involve training a smaller model on the output of DeepSeek-V3, effectively transferring the knowledge from the large model to a smaller one. Finally, consider using a multi-GPU setup if possible; however, the overhead of distributing such a large model across multiple GPUs is significant and may not be practical for all use cases.