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

Описание: 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...

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