Holy 💩. Tencent released a Local AI model dedicated to deciding what your AI agent should remember. This might become my favorite memory companion local AI model to pair with Qwen3.8-27B And community Q4 builds are already around ~17GB. 👀 Hy-Memory-30B-A3B is not meant to be a chatbot but rather a dedicated long-term memory writer. It 🧠 reads new conversations / agent activity 🎯 decides what is actually worth remembering 📝 turns evidence into precise persistent memories 🔄 updates memories when facts change 🗑️ avoids storing irrelevant information 💾 maintains the persistent memory bank Why? Without persistent memory, your agent either: 🧠 forgets useful information when the session/context ends 📚 keeps stuffing old conversations back into context A memory system gives your agent a compact long-term store that survives across sessions. Hy-Memory is the model deciding what deserves to go into that store. And today memory systems often use a general-purpose LLM for that job. Tencent instead trained a sparse specialist specifically for it. And Tencent reports it beats much larger frontier models at that specific task 🏆 Hy-Memory-30B-A3B → 55.49 Claude Opus 5 → 54.62 DeepSeek V4.1 Flash → 53.64 Qwen3.6-35B-A3B → 50.37 EverMemOS ranked #1 there at 56.89. Specs 🧠 30B total ⚡ ~3B active/token 🔓 Apache 2.0 🏠 open weights ⚙️ vLLM + SGLang 🔌 OpenAI-compatible API And community quants are already arriving: Q4_K_M → ~17GB Q5_K_M → ~20GB Q6_K → ~23GB EXL3 4.08bpw → ~15GB That puts it roughly in the same memory class as a Qwen3.8-27B Q4. Except Qwen3.8-27B is a dense general-purpose model. Hy-Memory activates only ~3B parameters/token and was trained specifically for remembering. 🔗 Link in ALT