Dwarkesh Patel

Interviewer and host of the Dwarkesh Patel podcast. In ^[src-042], he runs a blackboard-style technical interview with Reiner Pope on how frontier models are trained and served.

Key facts

• Type: Podcast host / technical interviewer
• Relevant source: “How GPT, Claude, and Gemini are actually trained and served”
• Format in this source: Long-form blackboard lecture focused on model architecture, ML infrastructure, inference economics, and training systems ^[src-042]
• Why it matters: The interview connects user-visible API behavior, such as fast modes, context pricing, and cache pricing, to low-level hardware constraints ^[src-042]

Related entities

• Reiner Pope
• MatX

Related concepts

• LLM Inference Economics
• Roofline Analysis for LLM Serving
• KV Cache

Source references

• ^[src-042] Dwarkesh Patel — “How GPT, Claude, and Gemini are actually trained and served – Reiner Pope” (2026-04-29)