Daedalus-150M: A Convolution-Attention Hybrid Designed for CPU Inference
Score
77提出从CPU部署约束反推架构设计的思路，并公开失败实验，信息增量明显。
实验设计严格，有预设基准、同规模对照和多项消融，证据链完整。
速度随上下文增长且内存恒定，对CPU端部署和长对话场景有直接参考价值。
论文于2026年8月发布，面向小模型CPU推理这一当前热点，时效性强。
来自arXiv预印本，数据与实验透明，但尚未经同行评审。
Base 85.5 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper is rigorously designed with a pre-fixed baseline, a controlled all-attention counterpart, and candid failure analysis, making it highly informative and actionable for edge deployment.
AI commentary
「What stands out here is not benchmark topping but using deployment constraints as the design starting point, not an afterthought. For teams working on edge AI, embedded systems, or privacy-preserving computing, the fact that the speed advantage grows with context is particularly valuable—longer conversations translate into bigger relative gains. If you have a CPU inference workload, it's worth reproducing its bandwidth analysis on your own hardware.」
Quick overview
Daedalus-150M is a convolution-attention hybrid small language model designed explicitly for CPU inference. Instead of building a large model and compressing it later, the authors fixed the target first: one user, one token at a time, 4-bit weights, and an ordinary CPU. The architecture uses full attention in only 6 of its 18 blocks; the other 12 rely on short convolutions whose cache stays two timesteps wide regardless of conversation length. Trained from scratch on 59.9B tokens, it scores 47.31 on a five-task benchmark against a 42.20 baseline, beating GPT-2 124M, Pythia-160M, OPT-125M, and GPT-neo-125M, and exceeding MobileLLM-125M's published score. Compared with a conventional all-attention model of the same size on the same data, the hybrid gains 0.81% quality, produces a 6.3% smaller 4-bit file, and decodes 1.76x faster at 2048 tokens of context. The speed edge grows with sequence length, which a simple bandwidth calculation cannot fully explain. The paper also candidly reports failures: unmitigated 4-bit quality loss, about half the convolution channels ending up inert, and an oversized vocabulary.
- Architecturally, only 6 of 18 blocks use full attention; the other 12 use short convolutions, keeping cache size constant regardless of context length.
- After training on 59.9B tokens, it scores 47.31 on a five-task benchmark, surpassing several larger models trained on more data.
- Compared with an equal-sized all-attention model, it produces a 6.3% smaller 4-bit file and decodes 1.76x faster at 2048-token context, with the edge growing as context lengthens.
- The paper candidly reports failures: unmitigated 4-bit quality loss, roughly half the convolution channels ending up inert, and an oversized vocabulary.
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