LLM Architecture in 2026: What You Need to Know with Sebastian Raschka

If you take a model release as an anchor point, let’s say Nemotron 3 or Qwen 3.5, you can go in both directions: You can either plug them into an agent and play around with that, or you can look, okay, what does the model look like under the hood? What are the ingredients? What type of attention mechanism do they use? What are currently research techniques that could make that even better in the next generation of models? What can we swap out, basically? And I’m interested in both of these!

Sebastian Raschka, Independent AI Researcher and author of Build a Large Language Model from Scratch, joins Hugo to talk about what’s changed in AI architecture, from post-training to hybrid models, and why understanding what’s under the hood matters more than ever for developers building in the agentic era. Sebastian’s upcoming book, Build a Reasoning Model from Scratch, currently available for pre-order on Amazon and in early access on Manning!

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We Discuss:

* Ed Tech for Agents: should we design educational content specifically for agentic systems, or is there a better approach?

* Inference Scaling is the new frontier, driving “gold-level” performance during generation via parallel sampling and internal meta-judges;

* Hybrid Architectures from Qwen 3.5 and Nemotron 3 scale almost linearly, making long-context agentic workflows significantly more affordable and performant;

* Multi-head Latent Attention (MLA), developed by DeepSeek, wins the KV cache war by drastically reducing memory overhead without performance hits;

* Agent Harnesses need to be continuously simplified as frontier models are post-trained on agent trajectories. Teams that don’t strip back their scaffolding risk the harness getting in the way of a more capable model.

* “AI Psychosis”: the cognitive load of supervising self-supervising agents, and why we’re all conducting an orchestra we were never trained to conduct;

* Sebastian’s AI Stack: a surprisingly simple setup (Mac mini, Codex, Ollama) with a ~20-item QA checklist, delegating the boring work to preserve energy for creative development;

* Fine-tuning is now an economic decision, optimizing costs and latency for high-volume tasks where long system prompts outweigh a one-time training run;