English(EN) Reasoning at Constant Memory: How a 4B Chinese Open Model Freezes the KV Cache at 72 MiB

EdgeRock-4B模型为长上下文推理削减KV缓存内存

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-23 12:01

一款名为EdgeRock-4B的新的40亿参数开源模型已发布，它显著降低了KV缓存内存使用量。该模型由Rock Matrix与香港科技大学和卡内基梅隆大学的研究人员合作开发，无论上下文长度如何，每个序列都保持固定的72MiB KV缓存大小，与Qwen3.5-4B等模型相比有了大幅度减少。EdgeRock-4B通过采用混合架构实现这一目标，该架构结合了循环状态、滑动窗口注意力和一种称为Rock Memory Anchors的新颖组件，旨在提高长上下文场景下的推理能力。 AI

影响该模型的内存效率有望为复杂推理任务的通用大上下文模型提供更易于访问且更具成本效益的部署。

排序理由来自研究合作的新模型发布，在LLM内存管理方面具有重大的技术创新。[lever_c_demoted from frontier_release: ic=1 ai=1.0]

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Towards AI TIER_1 English(EN) · TechSilk · 2026-10-23 12:01

Reasoning at Constant Memory: How a 4B Chinese Open Model Freezes the KV Cache at 72 MiB

<h4>Inside EdgeRock-4B’s “Rock Memory Anchors” — and what they mean for anyone serving long-reasoning models</h4><p>Every engineer who has served a reasoning model knows the moment. The model is good, the answers are right, and then someone sends a 64K-token agent trace and the G…

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Reasoning at Constant Memory: How a 4B Chinese Open Model Freezes the KV Cache at 72 MiB

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