A recent thread on Reddit, focused on the LocalLLaMA community, raised an interesting debate: how close are open-source models really to the state of the art (SOTA) in the field of natural language processing? The author of the post, rather than relying solely on traditional benchmarks, proposes an assessment based on direct experience.

Practical approach

The Reddit user emphasizes how benchmarks, while useful, do not always accurately reflect the performance of models in real-world use scenarios. The discussion therefore focuses on a more qualitative assessment, taking into account factors such as the flexibility, customization and adaptability of open-source models.

Considerations for on-premise deployment

For those evaluating on-premise deployment, there are significant trade-offs between using open-source models and proprietary solutions. Open-source models offer greater control over data and infrastructure, crucial elements for data sovereignty and regulatory compliance. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these trade-offs.