Community LLM reference – VRAM tables, GPU tier filter, tool-call ratings (bmt-llm-reference.vercel.app)

A new comprehensive reference guide has been released for community-driven Large Language Models (LLMs), detailing VRAM requirements, GPU tier filtering, and tool-call reliability ratings. This initiative provides an organized breakdown of various models based on VRAM categories—ranging from 6GB for smaller models up to 96GB for advanced multi-GPU models—with showcased capabilities from notable models, including application specializations and performance benchmarks. The guide aims to help practitioners select appropriate models suited to their hardware capabilities while also assessing tool reliability across diverse tasks. This development is significant for the AI/ML community as it addresses the growing demand for clearer specifications that can aid developers in optimizing LLM selection based on resource constraints and desired performance metrics. The detailed structure fosters better comparisons among models from various providers like OpenAI, Anthropic, and others, enabling more informed decisions for specific applications—be it coding, reasoning, or multimodal tasks. By enhancing accessibility and transparency in model selection, this reference serves as a valuable resource for researchers, developers, and organizations looking to leverage AI more effectively in their workflows.