Rise of the AI PC & local LLMs

Practical AI: Machine Learning, Data Science, LLM

We’ve seen a rise in interest recently and a number of major announcements related to local LLMs and AI PCs. NVIDIA, Apple, and Intel are getting into this along with models like the Phi family from Microsoft. In this episode, we dig into local AI tooling, frameworks, and optimizations to help you navigate this AI niche, and we talk about how this might impact AI adoption in the longer term.

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Featuring:

• Chris Benson – Twitter, GitHub, LinkedIn, Website
• Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

• Ollama
• LM Studio
• llama.cpp
• OpenVINO
• MLPerf client working group
• Article - 5 top small language models
• GPTQ article
• Article - Which quantization method is right for you

Something missing or broken? PRs welcome!

챕터

1. Welcome to Practical AI (00:00:00)

2. Correcting our mistakes (00:00:43)

3. Local offline AI PCs (00:03:34)

4. Local model applications (00:09:23)

5. Hosted vs Local (00:12:32)

6. Sponsor: Ladder Life Insurance (00:16:01)

https://ladderlife.com/changelog

7. Exploring local options (00:18:00)

8. AI PC prices (00:21:54)

9. Naming conventions (00:24:04)

10. Sponsor: Neo4j (00:25:40)

https://graphstuff.fm/episodes/2023-finale-llms-and-knowledge-graphs-throughout-the-year?&utm_campaign=UCGenAI&utm_content=AMS-SrDev-ToFuDev-UCGenAI-Audio-None-GenAI1-GenAI-NonABM&utm_medium=Audio&utm_source=PracticalAI&utm_justglobal=

11. Alphabet soup (00:26:56)

12. CPU derivitive (00:29:22)

13. Thanks for joining us! (00:34:09)

14. Outro (00:34:48)