# Launching the Fastest AI Inference Solution with Cerebras Systems CEO Andrew Feldman

Page: https://stenobird.com/podcast/gradient-dissent/launching-the-fastest-ai-inference-solution-with-cerebras-systems-ceo-andrew-feldman
Text version: https://stenobird.com/podcast/gradient-dissent/launching-the-fastest-ai-inference-solution-with-cerebras-systems-ceo-andrew-feldman.md
Podcast: [Gradient Dissent: Conversations on AI](https://stenobird.com/podcast/gradient-dissent)
Published: 2024-08-27T13:00:00+00:00
Episode link: https://wandb.ai/site/resources/podcast
Audio file: https://podcasts.captivate.fm/media/e861628d-57b2-4298-8cfd-cc5dd7d8af64/GD019-pod.mp3
Processing state: failed
JSON: https://stenobird.com/v1/public/podcasts/gradient-dissent/episodes/launching-the-fastest-ai-inference-solution-with-cerebras-systems-ceo-andrew-feldman
Duration seconds: 3194

## Resource

In this episode of Gradient Dissent, Andrew Feldman, CEO of Cerebras Systems, joins host Lukas Biewald to discuss the latest advancements in AI inference technology. They explore Cerebras Systems' groundbreaking new AI inference product, examining how their wafer-scale chips are setting new benchmarks in speed, accuracy, and cost efficiency. Andrew shares insights on the architectural innovations that make this possible and discusses the broader implications for AI workloads in production. This episode provides a comprehensive look at the cutting-edge of AI hardware and its impact on the future of machine learning. ✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYz 🎙 Get our podcasts on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/gd_google YouTube: http://wandb.me/youtube Connect with Andrew Feldman: https://www.linkedin.com/in/andrewdfeldman/ Follow Weights & Biases: https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server: https://discord.gg/CkZKRNnaf3 Paper Andrew referenced Paul David- Economic historian https://www.jstor.org/stable/2006600

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