Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput

Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a complex math problem, it can generate tens of thousands of tokens before arriving at an answer. Every one of those tokens must be stored in what is ... Link: https://www.marktechpost.com/2026/04/11/researchers-from-mit-nvidia-and-zhejiang-university-propose-triattention-a-kv-cache-compression-method-that-matches-full-attention-at-2-5x-higher-throughput/ :Cat-AI
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