NVIDIA Just Made Running 128K Token Models Actually Possible (KVzap Breakdown) скачать в хорошем качестве

NVIDIA Just Made Running 128K Token Models Actually Possible (KVzap Breakdown)

NVIDIA just open-sourced KVzap, and it's already changing how we run large AI models. This breakthrough compresses KV cache memory by 2-4x with almost zero accuracy loss - solving the biggest bottleneck in long-context AI. In this video, I break down: ✅ How KVzap cuts memory use by up to 75% while maintaining performance ✅ The exact benchmarks: RULER, LongBench, and AIME25 results across Qwen3-8B, Llama 3.1 8B, and Qwen3-32B ✅ Why this matters: a Llama 65B model with 128k tokens needs 335GB just for cache - KVzap slashes that ✅ How the surrogate model system works (linear layer vs MLP variants) KVzap vs competitors: Expected Attention, H2O, SnapKV, PyramidKV ✅ Real compression ratios: 2.7-3.5x average with threshold-based adaptive pruning ✅ Why it tops the KVpress Leaderboard for both prefilling AND decoding ✅ The 1.1% compute overhead secret that makes it practical ✅ How to implement it right now (it's fully open-source on GitHub) 🔗 LINKS: Official Paper: https://arxiv.org/pdf/2601.07891 GitHub Repo: https://github.com/NVIDIA/kvpress/tre... 💬 What do you think about KV cache compression becoming the new battleground in AI? Drop your thoughts below! 🔔 Subscribe for more AI breakthrough breakdowns 👍 Like if this helped you understand KVzap #NVIDIA #KVzap #AI #MachineLearning #LLM #KVCache #OpenSource #DeepLearning #TechNews #AINews