KVBuffer enhances linear attention serving for long-context AI models

Researchers have introduced KVBuffer, a novel serving mechanism designed to enhance the efficiency of linear attention models, particularly for long-context inference. This IO-aware approach buffers recent keys and values, enabling chunkwise computation and parallel verification for speculative decoding. Implemented within SGLang for models like Qwen 3.5 and Nemotron 3, KVBuffer demonstrates significant speedups in batch decoding and increases serving capacity for speculative decoding. AI

IMPACT Improves serving efficiency and latency for long-context AI models, potentially enabling wider adoption of linear attention architectures.

RANK_REASON This is a research paper detailing a new technical approach for serving AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• KVBuffer
• linear attention
• Longwei Zou
• Nemotron 3
• Qwen 3.5
• SGLang

• paper
• infra

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