#kvcaching — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #kvcaching, aggregated by home.social.
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New research shows KV‑cache compaction can slash LLM memory usage by up to 50× while preserving quality. With chunked processing and attention‑matching tricks, models like Llama 3.1 and Qwen‑3 handle far longer contexts—great news for open‑source and enterprise workloads. Dive into the benchmarks! #KVCaching #LLMMemory #LongContexts #ModelCompression
🔗 https://aidailypost.com/news/kv-cache-compaction-cuts-llm-memory-50-chunked-processing-long
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New research shows KV‑cache compaction can slash LLM memory usage by up to 50× while preserving quality. With chunked processing and attention‑matching tricks, models like Llama 3.1 and Qwen‑3 handle far longer contexts—great news for open‑source and enterprise workloads. Dive into the benchmarks! #KVCaching #LLMMemory #LongContexts #ModelCompression
🔗 https://aidailypost.com/news/kv-cache-compaction-cuts-llm-memory-50-chunked-processing-long
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New research shows KV‑cache compaction can slash LLM memory usage by up to 50× while preserving quality. With chunked processing and attention‑matching tricks, models like Llama 3.1 and Qwen‑3 handle far longer contexts—great news for open‑source and enterprise workloads. Dive into the benchmarks! #KVCaching #LLMMemory #LongContexts #ModelCompression
🔗 https://aidailypost.com/news/kv-cache-compaction-cuts-llm-memory-50-chunked-processing-long
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KV caching is a necessity on modern #LLMs, but it's not easy do to right. There's a literal zoo of techniques designed to handle it on many different levels. What to use and how are the benefits of each?
In this post I go through a recent survey article that collects and categorizes the most important KV caching techniques released in the last months. Brace yourself for a deep dive!
https://www.zansara.dev/posts/2025-10-26-kv-caching-optimizations-intro/
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Do you know how exactly prompt caching works in #GPT models? What is cached, at which stage? Let's have a deep dive into KV caching and how it makes your #LLM inference speed constant regardless of the prompt size.