Across long contexts, MiniMax-M2.1 (4-bit) leads in throughput, efficiency, and memory usage, while GLM-4.7 scales with higher cost.
Quantization still matters.
#LLM #AIResearch #MachineLearning #DeepLearning #GenerativeAI #Inference #ModelEfficiency #LongContext #Benchmarks
Across long contexts, MiniMax-M2.1 (4-bit) leads in throughput, efficiency, and memory usage, while GLM-4.7 scales with higher cost.
Quantization still matters.
#LLM #AIResearch #MachineLearning #DeepLearning #GenerativeAI #Inference #ModelEfficiency #LongContext #Benchmarks
📄 arXiv: 2512.20578
#AI #LLMs #MachineLearning #SelfAwareness #Interpretability #AIAlignment #NeurIPS #ICLR #DeepLearning
📄 arXiv: 2512.20578
#AI #LLMs #MachineLearning #SelfAwareness #Interpretability #AIAlignment #NeurIPS #ICLR #DeepLearning
A Wilf, P Aggarwal, B Parno, D Fried... [CMU] (2025)
arxiv.org/abs/2512.18160
#AI
#MachineLearning
#DeepLearning
#ArtificialIntelligence
#DataScience
#NeuralNetworks
#MLResearch
#AIResearch
A Wilf, P Aggarwal, B Parno, D Fried... [CMU] (2025)
arxiv.org/abs/2512.18160
#AI
#MachineLearning
#DeepLearning
#ArtificialIntelligence
#DataScience
#NeuralNetworks
#MLResearch
#AIResearch
📖 Learn more: arxiv.org/abs/2410.10429
📖 Learn more: arxiv.org/abs/2410.10429
#AI #VoiceAssistant #LangGraph #MCP #Privacy #ModularAI #LocalAI
#AI #VoiceAssistant #LangGraph #MCP #Privacy #ModularAI #LocalAI
#Blender
#3Danimation
#3DModel
#Blender
#3Danimation
#3DModel