DeepSeek V4.1-Flash powinien nazywać się V5. Po latach dominacji decoder-only encoder–decoder wraca do gry. Asymetryczna architektura Causal Encoder–Decoder wraz z optymalizacjami KV cache obniża koszt obsługi długiego kontekstu:
• 8B aktywnych parametrów/token w prefill, 16B w decode
• globalny KV cache ~890 B/token przy kwantyzacji głównego KV do FP4
• względem V4-Flash: około 1/4 pamięci HBM i 1/8 miejsca na SSD potrzebnych na KV cache.
🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more efficient.
🔹 Introducing the smallest model in our new architecture family, with native visual understanding.
🔹 Designed for greater capability, faster inference, higher throughput, and scaling to larger models.
1/6
Mimo że dostawcy AI szyfrują procesy myślowe modeli i odsyłają je klientom do stanowej pamięci szyfrem, badacze wykazali, że metadane API pozwalają odtworzyć pełny chain-of-thought z dokładnością 1:1
We can finally talk about it:
We found a way to extract hidden reasoning of frontier models using a vulnerability in the APIs of every frontier AI company.
We verified that our reasoning token count matches billed API thinking tokens 1:1 for most of the prompts we queried.
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