multi-token prediction
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LGAI-EXAONE/K-EXAONE-236B-A23B-GGUF Model Overview
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The LGAI-EXAONE/K-EXAONE-236B-A23B-GGUF model is a highly efficient AI architecture featuring a 236 billion parameter design with 23 billion active parameters, optimized with Multi-Token Prediction (MTP) for enhanced inference throughput. It supports a 256K context window using a hybrid attention scheme, significantly reducing memory usage for long-document processing. The model offers multilingual support across six languages with an improved 150k vocabulary for better token efficiency and demonstrates advanced tool-use and search capabilities through multi-agent strategies. Additionally, it is aligned with universal human values and incorporates Korean cultural contexts to address regional sensitivities, ensuring high reliability across diverse risk categories. This matters because it represents a significant advancement in AI efficiency, multilingual capabilities, and cultural sensitivity, potentially impacting various applications and industries.
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NVIDIA’s Blackwell Boosts AI Inference Performance
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NVIDIA's Blackwell architecture is delivering significant performance improvements for AI inference, particularly in handling the demands of sparse mixture-of-experts (MoE) models like DeepSeek-R1. By optimizing the entire technology stack, including GPUs, CPUs, networking, and software, NVIDIA enhances token throughput per watt, reducing costs and extending the productivity of existing infrastructure. Recent updates to the NVIDIA inference software stack, such as TensorRT-LLM, have increased throughput by up to 2.8x, leveraging innovations like NVFP4 data format and multi-token prediction (MTP). These advancements enable NVIDIA's platforms, like the GB200 NVL72 and HGX B200, to deliver industry-leading performance, efficiently supporting large AI models and enhancing user experiences. This matters because it allows AI platforms to serve more users with improved efficiency and reduced costs, driving broader adoption and innovation in AI applications.