Large models on CPUs

Practical AI: Machine Learning, Data Science

Model sizes are crazy these days with billions and billions of parameters. As Mark Kurtz explains in this episode, this makes inference slow and expensive despite the fact that up to 90%+ of the parameters don’t influence the outputs at all.

Mark helps us understand all of the practicalities and progress that is being made in model optimization and CPU inference, including the increasing opportunities to run LLMs and other Generative AI models on commodity hardware.

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Featuring:

• Mark Kurtz – Twitter, LinkedIn
• Daniel Whitenack – Twitter, GitHub, Website

Show Notes:

• Neural Magic
• SparseML
• SparseZoo
• Neural Magic Scales up MLPerf™ Inference v3.0 Performance With Demonstrated Power Efficiency; No GPUs Needed
• Deploy Optimized Hugging Face Models With DeepSparse and SparseZoo
• SparseGPT: Remove 100 Billion Parameters for Free

Something missing or broken? PRs welcome!

अध्यायों

1. Neural Magic Mark Kurtz (00:00:44)

2. Why does LLM size matter? (00:03:24)

3. GPUs vs. CPUs (00:06:15)

4. Overcoming perception (00:08:45)

5. Most parameters dont affect results (00:10:54)

6. Balancing space & sparsity (00:16:01)

7. Tackling performance hits (00:17:47)

8. Aware optimization vs not? (00:20:38)

9. Community tools (00:23:52)

10. Neural Magic tools (00:26:11)

11. Supporting new architecture (00:29:56)

12. Exciting research trends (00:31:40)

13. Looking forward in this space (00:34:52)

14. Outro (00:37:05)