Promethevs: Full DeepSeek-v4.1-Flash-1017, locally, on 12GB of VRAM (github.com)

The newly released PROMETHEVS engine enables users to run the DeepSeek-V4.1-Flash-1017 model locally on standard PCs, requiring only a 12 GB NVIDIA graphics card and 64 GB of RAM. This open-source model leverages a compress-then-correct method developed by Ryan Flavell to deliver near-full model performance at significantly faster speeds—up to 7.4 tokens per second for code prompts—compared to the original DeepSeek model, which averages around 2.14 tokens per second under the same conditions. The technique involves computing outputs using a compressed 2-bit representation, with the model automatically recalibrating the results when confidence levels fluctuate. The significance of PROMETHEVS lies in its democratization of advanced AI capabilities, allowing users to harness a powerful model without needing expensive cloud services or high-end hardware. By putting frontier-level AI within reach of everyday users, it challenges the dominance of proprietary cloud-based AI systems, promoting a more accessible and private AI ecosystem. With private operation on local machines, users maintain complete control over their data while benefitting from the reliability and rapid response of the model.