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Raytention is a new attention mechanism that solves the bloated k-v cache VRAM problem. Raytention utilizes signals built from the context to provide the model with the attention it needs at a much lower VRAM cost.

1 starsPython

Raytention Updated

by NohWai·Jul 19, 2026·1 point·0 comments
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AI Analysis

○PassBold Bet

Claims zero KV cache via Euclidean distance but lacks code, benchmarks, or training runs.

Strengths
  • •Geometric interpretation of attention offers a theoretically interesting alternative to dot products.
  • •Table comparing memory usage across mechanisms clearly illustrates the potential VRAM savings.
Weaknesses
  • •No implementation code, training logs, or perplexity benchmarks provided to verify claims.
  • •Replacing dot products with L2 distance fundamentally breaks softmax probability distribution properties.
Category
AI/ML
Target Audience

ML researchers and LLM infrastructure engineers

Similar To

Linear Attention · Performer · RetNet

Post Description

Here is the easier to read and navigate repo for Raytention, with a more simple version to drop into your training loop to try it out.

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