AIKVMEM reportedly supports a million-token agent workspace—not a million-token active prompt
A post describes KVMEM as reusing cached model state stored in GPU memory, RAM or NVMe storage. It reports gains in task success and recovery speed, while stressing that each step sees only a bounded slice.
TLDR
A post describes KVMEM as retaining previously processed context in reusable KV caches and retrieving only the pieces an agent needs for its current step. On DeepSWE with Qwen3.8-27B, it reports Pass@1 rising from 43.8% with compaction alone to 48.4% with KVMEM. Across controlled benchmarks, it reports recovery 11.4–53.8× faster than Compact+RAG. The post also reports a million-token workspace running at about 50 tokens per second on a laptop with a 24 GB RTX 5090 GPU. It explicitly distinguishes that workspace from a million-token active prompt: each step still sees only a bounded slice.
