NVIDIA Integrates TriAttention: Trigonometric KV Compression for Long Context
青稞AI · wechat · 2026-08-20
To address the VRAM bottleneck of KV Cache in long reasoning, NVIDIA researchers proposed TriAttention. The method leverages the observation that pre-RoPE Q/K vectors cluster tightly around a fixed center. By approximating attention logits with trigonometric series based on this centrality, TriAttention effectively scores key importance for cache pruning. It has been merged into NVIDIA TensorRT-LLM and adopted by the LongLive video generation framework, reducing local-attention KV memory by 50% without quality loss.
More from Infra
- SK hynix preps for post-HBM era with 3D stacking shift — zephyr_z9 · 2026-08-20
- Guangdong, Alibaba Sign Strategic Pact to Boost AI, Chips and Computing Power — pstAsiatech · 2026-08-20
- GPU Market Pricing Chaos: Spreads Wide Across Venues — AccBalanced · 2026-08-20
- Dual RTX 3090 running Qwen3.8-27B locally at 50-65 tok/s — is that normal? — sugarfreecaffeine · 2026-08-20
- AVX-512 Deemed Superior to ARM SVE: Register & Masking Edge — lemire · 2026-08-20
- Modular open sources platform repo integrating MAX engine and Mojo language — modular · 2026-08-20