korrents
Jay Alammar

What Jay Alammar thinks about LLMs

@jay-alammar · 7 positions · On ppll ↗

Writes Language Models & Co., a newsletter on large language models; known for The Illustrated Transformer, referenced in AI/ML courses at MIT, Stanford and Cornell.

Jay Alammar did not write this page.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

Filter LLMs · Newest first
Topic
All 7LLMs 4AI agents 1open source 1
Sort
NewestOldest

Only Jay Alammar on LLMs. See everyone on LLMs

4 dated positions, 2024 to 2025, in their own words. Our reading of what Jay Alammar has said — not written or endorsed by them.

4 positions so far — this page is not yet offered to search engines.

  1. Our readingRunning large batches of prompts through an LLM API is a capability unavailable to someone who only uses models through a chat playground interface.

    Their wordsRunning thousands of prompts is one of the superpowers you do not get to use if all of your LLM usage is through a playground.

    ↗Inside NeurIPS 2025: The Year’s AI Research, Mappednewsletter.languagemodels.co 1st of 2 in this piece

  2. 2 months earlier

  3. Our readingMore reasoning gives a language model more time and compute to solve harder problems, but increases latency and compute cost.

    Their wordsOn the one hand, more reasoning allows the model more time and compute to reason about a problem which helps it tackle more difficult problems. On the other hand, that comes at a cost of latency and compute.

    ↗The Illustrated GPT-OSSnewsletter.languagemodels.co 2nd of 2 in this piece

  4. 7 months earlier

  5. Our readingBase large language models have now crossed a threshold of quality and capability significant enough to change what training methods are viable.

    Their wordsModern base models have crossed a certain threshold of quality and capability (this base model was trained on 14.8 trillion high-quality tokens).

    ↗The Illustrated DeepSeek-R1newsletter.languagemodels.co 1st of 2 in this piece

  6. 3 months earlier

  7. Our readingLanguage models are not merely text generators; they can also form other systems, such as embeddings and classifiers, that are useful for problem solving.

    Their wordsA central concept the general public should know is that language models are not merely text generators, but that they can form other systems (embedding, classification) that are useful for problem solving.

    ↗Our book, Hands-On Large Language Models, Is Now Out!newsletter.languagemodels.co