The LocalLLaMA community is buzzing about the upcoming release of new large language models (LLMs) with sizes of 9 and 35 billion parameters.

Optimization for Local Environments

The discussion, originating on Reddit, highlights users' interest in models that can be run efficiently on consumer hardware or in on-premise environments with limited resources. The goal is to find a balance between model size and performance, to achieve acceptable results without requiring excessively expensive infrastructure.

For those evaluating on-premise deployments, there are significant trade-offs between initial hardware cost, energy consumption, and cooling requirements. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these trade-offs.