Part 6/13:
Benchmark scores reveal ML2's competitive edge: it scores 84% on the Massive Multitask Language Understanding (MLU) benchmark—though slightly behind Meta’s Llama 3.1 at 88.6%, GPT-4 at roughly 88.7%, and Claude 3.5. Crucially, ML2 achieves this with significantly fewer resources—requiring about 246 GB of memory to operate at full precision, making it more efficient and accessible for deployment on multi-GPU servers or even high-end personal machines.