Show Notes
In this MonkCast conversation, RedMonk's James Governor talks with Paul Brookes, a senior AI engineer at TurinTech, about making AI inference faster and cheaper. TurinTech predates the generative AI boom, having spent years optimizing complex code with genetic algorithms, and now points those tools at the models themselves. Brookes walks through techniques like kernel fusion and model compilation that squeeze more tokens per second out of specific hardware, drawing on the company's work with Intel on OpenVINO and vLLM. The two get into running capable open models such as Qwen on local machines, the spiraling cost of AI , and why judging engineers by tokens burned misses the point. Brookes also describes Artemis and its discovery harness, which lets agents learn from past results, and traces his own route from quantum physics into low-level performance engineering.
This RedMonk conversation is sponsored by TurinTech.
Show notes: https://redmonk.com/videos/paul-brookes/
Chapters 00:00 Introduction to AI and TurinTech 01:42 Optimization Challenges in AI 04:30 Working with Semiconductor Companies 08:46 The Cost of AI and Local Model Deployment 11:29 Local Model Performance and Infrastructure 14:44 Partnerships and Breakthroughs with Intel 16:54 Key Techniques for Optimizing Inference 19:39 The Role of AI Engineers 21:50 Bridging the Gap with Customers 23:21 Harnessing AI for Continuous Improvement
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