Show Notes
This episode addresses the real bottleneck after you build an LLM: fitting it into hardware that can actually run it. We explore why GPU memory is the scarce resource, how weights, KV cache, and activations compete for that space, and what that means in practice when prompts get long or concurrency spikes. We compare data center GPUs (high bandwidth HBM) versus local machines like the Mac Studio (huge unified memory but slower bandwidth) to show the core tradeoff between capacity and speed. By the end, you will understand how to choose hardware based on your goal, and why the next lever is quantization to shrink models enough to fit, with a closing reflection on perspective when something big feels like it will not fit.
More from The AI Concepts Podcast
In this episode, we bring everything together by following one LLM application from the user's first request to the final response. We see retrieval, context, tools, model calls, state, memory and orchestration working as one system, revealing the bigger lesson of the module: building with LLMs is not just about what the model can do, but deciding what the model should do and what the rest of the application should handle.
In this episode, we map the modern LLM application stack and explore where frameworks, SDKs, runtimes and protocols actually fit. We look at how tools like LangChain, LangGraph, LlamaIndex, provider SDKs, durable workflows and MCP solve different problems, and why understanding the architecture matters more than memorizing product names.
This episode separates two concepts that are often confused: state, which keeps track of what is happening now, and memory, which allows information from the past to become useful later. We explore how applications create continuity around a model, and why good memory is not about remembering everything, but remembering the right things at the right time.
This episode breaks down what orchestration actually means, from sequencing and routing to retries, parallel execution and human approvals, and explores the different ways those flows can be managed. Most importantly, we separate orchestration from the model itself and show why it is really about controlling how work moves through an application.
Who actually decides what happens next inside an LLM application? This episode explores the difference between decisions made by code and decisions made by the model, and why real applications often use both. We follow the loop that emerges when a model can request information or actions, receive the results and decide what to do next, revealing what developers mean when they talk about “owning the loop” and setting the stage for orchestration.