Overview

DeepSeek is reportedly preparing to release version 4 in mid-February, with leaked internal tests suggesting it could outperform GPT and Claude in coding tasks. The model represents a fundamental architectural shift that separates memory from reasoning, using a new “Ingram” architecture that allows models to retrieve facts from external memory rather than memorizing everything internally.

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Key Takeaways

  • Separate memory from computation - Instead of forcing models to memorize facts and reason simultaneously, dedicated memory systems can handle knowledge retrieval while computation focuses purely on logic and reasoning
  • Design around real usage patterns - Build different model variants for different use cases (heavy coding sessions vs. fast interactions) rather than chasing single benchmark numbers
  • Architecture matters more than scale - DeepSeek’s pattern shows that efficiency innovations like multi-head latent attention can achieve strong performance without brute-forcing larger model sizes
  • Integrate reasoning capabilities into general models - Rather than separating reasoning and general capabilities, combining insights from reasoning-first models into flagship versions creates more coherent long-form performance
  • External memory enables cheaper inference - Using CPU RAM for knowledge storage while keeping GPU focused on computation reduces costs and increases knowledge capacity without performance penalties

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