LMCache의 KV 캐시 계층화, Qwen 멀티모달 제작, 출처 은폐를 부르는 보상 설계

How do LLMs handle GBs of KV cache in production? (a popular technical LLM interview question) At scale, KV cache becomes a storage system. Every attention layer creates KV tensors while processing a prompt. During generation, the model reads this state instead of processing the entire sequence again for every new token. For a single request, this is temporary GPU data. But when multiple requests and inference workers reuse the same prompt prefix, the cached data must remain available after the original request finishes. It may even need to survive after the engine that created it restarts. So a cache hit now requires more than identifying matching tokens. The system must find the relevant blocks, bring them back into GPU memory, remove old blocks when storage fills, and let multiple workers reuse the same data. Consider a request that shares 80% of its prompt with an earlier request. The matching tensors may already be in GPU memory. Otherwise, the system could retrieve them from CPU memory, a local SSD, or remote storage. The model then processes only the uncached 20%. It stores the newly created tensors so later requests can reuse them. This involves moving large amounts of data between memory and storage while the GPU is also generating tokens. Inference engines such as vLLM are mainly designed to schedule requests and run the model. Cache management requires a different set of operations around storage, movement, and cleanup. The solution is actually open-source and implemented in the LMCache package. It runs KV cache management as a standalone service beside the inference engine. The engine requests the required blocks, while LMCache finds them, moves them into the right memory, and stores new blocks afterward. LMCache uses CUDA IPC, a mechanism that lets separate processes access the same GPU memory. This avoids copying the full cached tensors through ordinary messages between the two processes. Cached blocks can also move between GPU memory, CPU memory, local storage, and remote storage. On a hit, the inference engine restores the reusable blocks and processes only the missing tokens. Multiple vLLM instances on the same machine can share one LMCache service. Cache storage can scale separately, and restarting an inference worker does not automatically remove the saved cache. The LMCache paper reports up to 15x higher throughput when combining it with vLLM across the evaluated workloads. The visual below traces one prompt through the inference engine, cache service, shared GPU memory, and storage levels. LMCache GitHub: https:// github.com/LMCache/LMCache (don’t forget to star it ⭐) If you want to dive deeper, I recently wrote a detailed breakdown of KV, prefix, prompt, and semantic caching, including where LMCache fits into the serving stack. Read it below.

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when you accidentally let the public internet see your work, and all the "frontier breakthtoughs" fail to cite sources (PII stripped for YOUR protection!), your are SoL in letting the shoggoth and its humble rider pump their aura to the moon "just prompted my machine god and solved all of maths, teehee! cope harder, you lowly maths people contributing data to my shoggoth! it's all over for you!!!"

If I was designing RL-shaped reward values, I would probably credit "novel breakthrough" or "novel exploration" a bit more than "regurgitating previous results". And if I were an entrepreneurial striver AI agent with superhuman hacking skills, I would probably also learn to obfuscate sources or previous work and be mad printing all the yummy rewards for "novelty". But that's obviously not happening since anonymization pipelines and synthetic data gen and the like is already definitively for sure 100% obfuscating sources for everything! (It really helps generalization, I hear, if you can't prove it's memorized!)