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Explore The KV Cache Layer That Makes LLMs 10x Faster? (LMCache) with 30,904 views, 317 likes, and 17 comments. Experience the impact of this video content that has captured audience attention.

Did you know that multi-turn coding agents re-send 93% to 97% identical prompt context on every single turn, forcing your GPU to waste 95% of its compute re-reading tokens from scratch? In this deep dive, Cloud Codes analyzes LMCache, the open-source tiered KV-cache layer created by researchers at the University of Chicago and Tensormesh (backed by NVIDIA, AMD, and CoreWeave), that decouples the KV cache from vLLM and SGLang into a shared, multi-tiered storage hierarchy. 🔔 Subscribe: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w?sub_confirmation=1 💙 Become a Member: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w/join 🐦 Twitter/X: https://x.com/cloud_codes 💬 Discord: https://discord.gg/4kJqEBMMf We break down the hardware bottlenecks of autoregressive prefill vs decode, examine Google GKE benchmarks cutting Time-to-First-Token (TTFT) by 79% while raising input throughput by +264%, and explore how multi-process cache sharing drops 8-GPU TTFT from 3.98s to 0.29s (a ~13x leap). Furthermore, we audit the honest failure cases from AMD synthetic sweeps showing why LMCache acts as a 10% to 17% performance tax when GPU memory isn't pressured, locate the exact 250k-to-300k token working set crossover, and troubleshoot critical deployment gotchas like PYTHONHASHSEED and context truncation. If this breakdown helped you master LLM serving, vLLM architecture, and KV-cache optimization, subscribe to Cloud Codes for a new infrastructure deep dive every week! Build, solve, deploy. 🔗 Repositories & Sources Mentioned: • LMCache GitHub Repository: https://github.com/LMCache/LMCache • LMCache arXiv Paper (2510.09665): https://arxiv.org/abs/2510.09665 • Google GKE & LMCache Tiered Cache Benchmark: https://blog.lmcache.ai/ • Tensormesh Enterprise Platform: https://tensormesh.ai/ • CacheBlend: Fast Non-Prefix KV Cache Reuse (arXiv:2405.16444): https://arxiv.org/abs/2405.16444 • vLLM Official Serving Framework: https://github.com/vllm-project/vllm ⏱️ Video Chapters: 0:00 - The 95% Wasted Compute in LLM Prefill 0:35 - What Prefill and the KV Cache Actually Cost 1:16 - 739 Real Agent Traces: The 97% Reusability Metric 2:09 - vLLM Prefix Caching vs The Card Memory Ceiling 3:54 - Meet LMCache: Tiering KV from HBM to Object Storage 4:31 - The Google GKE Benchmark: 79% Lower TTFT & 264% Throughput 5:04 - Multi-Process Rebuild: The 13x Time-to-First-Token Leap 6:51 - When Caching Becomes a Tax: The 250k-Token Crossover 8:40 - Silent Gotchas: Hash Seeds and Context Truncation Traps 9:58 - Why the Industry Converged: PyTorch, GKE & Tensormesh 10:51 - The Final Verdict: When to Install LMCache #llminference #vllm #lmcache #kvcache #sglang #machinelearning #deeplearning #gpu #aiinfrastructure #cloudcodes User Queries: how to use lmcache with vllm for fast inference what is lmcache tiered kv cache architecture reduce time to first token ttft long context llm vllm automatic prefix caching vs lmcache comparison cacheblend lmcache non prefix kv cache reuse lmcache 250k token crossover memory pressure how to share kv cache across multiple vllm instances speed up multi turn coding agents prefill latency lmcache s3 remote object storage kv cache speed tensormesh zero dollar cached input tokens pricing

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