Вісті Економіки | #УкрТг

і щоб закінчити вже з DeepSeek на сьогодні. Побачив тут експлейнер по тому, чому DeepSeek зміг бути таким ефективним і за менші кошти, і згадав вислів: потрібно працювати не по 12 годин, а головою)

1/ First, some context: Right now, training top AI models is INSANELY expensive. OpenAI, Anthropic, etc. spend $100M+ just on compute. They need massive data centers with thousands of $40K GPUs. It's like needing a whole power plant to run a factory.

2/ DeepSeek just showed up and said "LOL what if we did this for $5M instead?" And they didn't just talk - they actually DID it. Their models match or beat GPT-4 and Claude on many tasks. The AI world is (as my teenagers say) shook.

3/ How? They rethought everything from the ground up. Traditional AI is like writing every number with 32 decimal places. DeepSeek was like "what if we just used 8? It's still accurate enough!" Boom - 75% less memory needed.

4/ Then there's their "multi-token" system. Normal AI reads like a first-grader: "The... cat... sat..." DeepSeek reads in whole phrases at once. 2x faster, 90% as accurate. When you're processing billions of words, this MATTERS.

5/ But here's the really clever bit: They built an "expert system." Instead of one massive AI trying to know everything (like having one person be a doctor, lawyer, AND engineer), they have specialized experts that only wake up when needed.

6/ Traditional models? All 1.8 trillion parameters active ALL THE TIME. DeepSeek? 671B total but only 37B active at once. It's like having a huge team but only calling in the experts you actually need for each task.

7/ The results are mind-blowing: - Training cost: $100M → $5M - GPUs needed: 100,000 → 2,000 - API costs: 95% cheaper - Can run on gaming GPUs instead of data center hardware