Large models on CPUs
from Practical AI Hosted by Daniel Whitenack and Chris Benson
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. Sponsors: Fastly – Our bandwidth partner. Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com Fly.io – The home of Changelog.com — Deploy your apps and databases close to your users. In minutes you can run your Ruby, Go, Node, Deno, Python, or Elixir app (and databases!) all over the world. No ops required. Learn more at fly.io/changelog and check out the speedrun in their docs. Featuring: Mark Kurtz – LinkedIn, X Daniel Whitenack – Website, GitHub, X 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 Upcoming Events: Register for upcoming webinars here!
Embed this episode
Chapters
- 0:43 Neural Magic Mark Kurtz
- 3:24 Why does LLM size matter?
- 6:15 GPUs vs. CPUs
- 8:45 Overcoming perception
- 10:53 Most parameters dont affect results
- 16:01 Balancing space & sparsity
- 17:46 Tackling performance hits
- 20:37 Aware optimization vs not?
- 23:51 Community tools
- 26:11 Neural Magic tools
- 29:56 Supporting new architecture
- 31:40 Exciting research trends
- 34:52 Looking forward in this space
- 37:04 Outro
No transcript yet.