EdgeRock-4B Opens With Fixed-Memory Long-Context Inference
Rock Matrix, Hong Kong University of Science and Technology, Carnegie Mellon University and other teams released and open-sourced EdgeRock-4B, according to QbitAI. The Qwen3.5-4B-based model uses recurrent state, a local window and Rock Memory Anchors to keep KV Cache at 72 MiB under a 256K context, reducing it by 99.1% from the source model. In team-reported tests, it ran 512 concurrent long-inference requests on one 80GB Hopper GPU at 12,124 tokens/s and averaged an 82.9% one-pass rate on AIME 2024–2026; no independent replication was provided.
EdgeRock-4B combines fixed-capacity long-context state with text and image inference in an open-source release, offering a notable option for reducing KV Cache memory and improving single-GPU concurrency.
KV Cache砍掉99%，单卡吞吐飙6倍，港科CMU联手岩山科技开源新模型 – 量子位
< img id="wx_img" src="https://www.qbitai.com/wp-content/uploads/imgs/qbitai-logo-1.png" width="400" height="400">
KV Cache砍掉99%，单卡吞吐飙6倍，港科CMU联手岩山科技开源新模型
一张卡，512路长推理同时跑
#EdgeRock-4B#Rock Matrix#Hong Kong University of Science and Technology#Carnegie Mellon University#Qwen3.5-4B#Rock Memory Anchors