Programming Tutorials

  The release of DeepSeek-V3 has shifted the landscape of open-weights LLMs, offering GPT-4 class performance in a Mixture-of-Experts (MoE) architecture. However, excitement often crashes into a hard wall: the   torch.cuda.OutOfMemoryError . If you are trying to run the full 671B parameter model on a consumer rig—even a high-end dual RTX 4090 setup—you are likely failing. The confusion stems from a misunderstanding of how MoE models consume memory versus how they consume compute. This guide provides a root cause analysis of the VRAM bottleneck, the realistic hardware math required to run DeepSeek-V3, and a Python implementation for dynamic GPU/CPU offloading to run this giant locally. The Root Cause: MoE Storage vs. Compute The most common misconception with DeepSeek-V3 is confusing  Active Parameters  with  Total Parameters . DeepSeek-V3 uses a Mixture-of-Experts architecture. It has  671 billion total parameters , but only activates approximately  37...