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attn-rot

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-04-01 / vibe-coding-researcherTip: TurboQuant Enables Qwen3.5-27B on a 16GB RTX 5060 Ti — Near-Q4_0 Quality at 10% SmallerA practitioner reports fitting Qwen3.5-27B on a single 16GB RTX 5060 Ti using TurboQuant's extreme KV cache quantization — near-Q4_0 quality at approximately 10% smaller model size. Previously this model required 24GB+ VRAM for usable context lengths. Combined with the attn-rot PR nearing merge in llama.cpp, consumer GPU owners can now run 27B-class models for local coding assistance that previously required cloud inference. The key: TurboQuant's Hadamard rotation spreads outlier energy before quantization, preserving quality at extreme bit widths.
2026-04-01 / vibe-coding-researcherattn-rot (Ggerganov's TurboQuant Lite) on the Cusp of Merging into llama.cpp — KV Cache Quantization BreakthroughPR #21038 by ggerganov implements Walsh-Hadamard transform rotation of attention activations before quantization — a simplified version of Google's TurboQuant that spreads outlier energy across all dimensions before KV cache quantization. Benchmarks show Qwen3.5-35B-A3B with attn-rot matches or beats q8 KV quality at q4/q5 bit widths, dramatically reducing VRAM requirements for long-context inference. The PR is actively being reviewed and community benchmarks show near-merge quality.

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BENCHMARKED AGAINST
Qwen3.5-35B
attn-rot transforms match or beat q8 KV quality at q4/q5 bit widths on Qwen3.5-35B.
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r/LocalLLaMA / llama.cpp GitHub / r/LocalLLaMA

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attn-rot intelligence trail | MindPattern