r/LocalLLaMA Community Discusses Archiving Relevant Open-Weights AI Models from 2024–2025
A user on r/LocalLLaMA asked the community to recommend open-weights AI models from 2024 and 2025, such as DeepSeek-R1, that remain valuable enough to archive locally. The post highlights how the rapid shift toward Mixture of Experts (MoE) architectures is making many older dense models obsolete. As AI models evolve rapidly, self-hosted AI enthusiasts face decisions on which older open-weights models are worth preserving for privacy, offline use, or unique performance characteristics. This reflects broader ecosystem discussions regarding storage management and model longevity in local hardware environments. The original poster noted having 28TB of dedicated local storage, allowing them to host high-parameter models rather than relying on low-precision quantizations. Key examples mentioned include standout reasoning models like DeepSeek-R1 that continue to provide utility despite newer releases.
## BACKGROUND
Open-weights LLMs enable individuals to run generative AI locally on personal hardware without relying on external cloud APIs. Dense models process every input token through all model parameters, whereas Mixture of Experts (MoE) architectures route inputs to specific subnetworks, offering higher compute efficiency for local inference.