About this episode

This episode discusses the safe use of open weight large language models in enterprises and the associated risks and considerations.

In this podcast episode James Wilson and Brad Arkin talk about how to safely use open weight large language models in the enterprise. The cost of frontier models was already driving interest in freely available open weight models like DeepSeek, Kimi and Qwen. But now the US government is forcing Anthropic to pull its Fable and Mythors models from the market, the argument for having greater control over your own AI stack is stronger than ever. But as you’ll hear in this episode, the model itself is just one component of the complex tech stack you’ll need to spin up if you want local inference. There’s a lot of moving parts, each of which comes with its own supply chain risks. So whether you’re hosting these models on your own hardware or via a SaaS provider, there’s a lot to ponder!

People in this episode

Guests: James Wilson, Brad Arkin

Topics covered

• open weight models
• large language models
• AI stack control
• supply chain risks
• local inference
• enterprise AI

Keywords

• open weight models
• large language models
• AI stack
• supply chain
• local inference
• enterprise AI

Mentioned in this episode

Organizations: Anthropic

Products: Fable, Mythors, DeepSeek, Kimi, Qwen

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