DeepSeek Rebuilt the Transformer for Efficiency
DeepSeek V4.1 Flash is built around one idea: make long-context AI dramatically more efficient. In this video, I break down how DeepSeek shrinks KV-cache memory to just 890 bytes per token, the architectural changes that make it possible, why this matters for long-running agents and huge codebases, and where the model still falls short against frontier systems. LINKS: Blogpost: https://www.deepseek.com/en/news/deepseek-v4-1-flash/ Huggingface: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash ENGRAM: https://youtu.be/zt1jlTPCaps DSpark: https://youtu.be/eFgknPFK-g0 My voice to text App: whryte.com Website: https://engineerprompt.ai/ RAG Beyond Basics Course: https://prompt-s-site.thinkific.com/courses/rag Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 💻 Pre-configured localGPT VM: https://bit.ly/localGPT (use Code: PromptEngineering for 50% off). Signup for Newsletter, localgpt: https://tally.so/r/3y9bb0 00:00 V4.1 Flash Breakthrough 01:38 Why KV Cache Hurts 02:37 Prefill vs Decode Compute 03:55 Bet One CED Split 05:06 CSA2 Layer Sharing 06:27 4 Bit Quantization Replay 10:15 Benchmarks Takeaways
