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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. Same week, small update: Run LLMs Locally Multi-Token-Prediction (MTP) for Gemma-4-E4B and Gemma-4-26B from Unsloth. After 50% from QAT, this brings another 25-

    A recent update to the "Run LLMs Locally" project has introduced Multi-Token-Prediction (MTP) for Gemma models, achieving speed improvements of up to 90% in token generation. This optimization, combined with Quantization-Aware Training (QAT), has led to significant performance gains for local LLM execution. Additionally, prompt sizes have been reduced by 60% through configuration adjustments, and logging of all prompts has been implemented. AI

    IMPACT These optimizations for local LLM execution could lower the barrier to entry for advanced AI applications, enabling more users to run powerful models on consumer hardware.