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 called the KV cache […]
The post Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput appeared first on MarkTechPost.
