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Research questionHow can hybrid language models aggressively quantize Gated DeltaNet layers while preserving quality and long-context behavior?Hybrid language models combine attention with recurrent layers whose state is updated across tokens. Low-bit quantization may introduce errors that accumulate or alter long-context behavior, while its effects on inference resources also need to be understood.
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Latest papersRecent research connected to this question, newest first.Why Gated DeltaNet Survives 4-Bit Quantization: NVFP4 W4A4 for the Recurrent Half of a Hybrid 27B LLMThis paper evaluates low-bit weight-and-activation quantization across the linear layers of a 27B hybrid model, including its Gated DeltaNet layers, with calibrated KV-cache scales and compatible serving kernels. Evidence is limited to the reported model, calibration procedure, kernels, context lengths, and quality and retrieval evaluations.research paper · Sep 3, 2026
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