2. Accounting tricks aside, cutting training costs and still getting results on par with o1 (but reportedly not o3) is an impressive feat. But … 3. Training is only part of an LLMs cost; inference (actually running the LLM to do stuff) is a much bigger expense at scale. 🧵3/7 - ThreadSky

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crispen.bsky.social • 34 days ago

2. Accounting tricks aside, cutting training costs and still getting results on par with o1 (but reportedly not o3) is an impressive feat. But …

3. Training is only part of an LLMs cost; inference (actually running the LLM to do stuff) is a much bigger expense at scale. 🧵3/7

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crispen.bsky.social•34 days ago

4. But if you don’t want to scale this, being able to run a 32-billion-parameter LLM on a home computer’s 24 GB graphics card is an amazing breakthrough. 🧵4/7

crispen.bsky.social•34 days ago

5. The fact that DeepSeek released this under the MIT Open Source license means that everyone can try to reverse engineer this and apply it to other LLMs. If the lessons can be transferred? AI everywhere, baby! If not? See: Fleischmann–Pons cold fusion. 🧵5/7

crispen.bsky.social•34 days ago

6. DeepSeek dot com is tantamount to ChatCCP — everything you upload to it is stored on servers in China. If you run it locally (locally means downloading and installing it on your computer, not accessing it through an app on your phone) ... 🧵6/7

crispen.bsky.social•34 days ago

you keep your content and interactions local, but either way the LLM has been nerfed with real-time censorship of content related to sensitive topics the CCP wants to avoid (see: Winnie-the-Pooh).

7. You should have bought the dip. :) 🧵7/7

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