Learning path

Training Infrastructure: The Stack Behind Modern AI

Building and training large AI models doesn't happen in a vacuum — it takes hardware, cloud platforms, software tooling, and architectural choices that all fit together. This path traces that stack from the chips up: who makes the hardware, who provides the compute, who builds the tools, and how leading labs put it all to use. Aimed at practitioners who want to understand the full picture, not just the models themselves.

In-depth11 steps~56 min

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NVIDIA
Start at the silicon layer: NVIDIA's GPUs are the dominant compute substrate for training, so understanding what they offer sets the foundation for everything above.
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Intel
The competitive alternative in AI hardware — understanding Intel's position clarifies why the chip market looks the way it does and where the pressure on NVIDIA comes from.
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Amazon Web Services
With hardware in mind, this covers how cloud providers like AWS package and deliver that compute at scale — the layer most teams actually interact with.
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Microsoft
Microsoft's deep integration with OpenAI and its Azure AI infrastructure makes it a central node in how frontier training compute is actually provisioned.
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Hugging Face Transformers
Moving up the stack: Hugging Face Transformers is the most widely used library for working with models, bridging raw compute and usable training pipelines.
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Hugging Face
Hugging Face as a platform — the hub, datasets, and ecosystem that sits on top of the library and shapes how models are shared and fine-tuned across the community.
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Mixture of Experts
A key architectural choice that shapes training cost and inference efficiency — understanding Mixture of Experts explains why frontier models are designed the way they are.
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DeepSeek V4
DeepSeek V4 is a concrete example of how infrastructure choices — MoE architecture, efficient training runs — translate into a competitive frontier model.
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Meta
Meta's open-weight approach and its investment in custom training infrastructure offer a contrasting model to closed labs — important context for the broader ecosystem.
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OpenAI
OpenAI's training infrastructure decisions — from compute partnerships to scaling strategy — have set many of the benchmarks the rest of the field responds to.
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Anthropic
Anthropic's approach to training, including its safety-focused methodology and Constitutional AI, rounds out the picture of how different labs make different infrastructure and process tradeoffs.
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Training Infrastructure: The Stack Behind Modern AI

In-depth11 steps~56 min

NVIDIA

Intel

Amazon Web Services

Microsoft

Hugging Face Transformers

Hugging Face

Mixture of Experts

DeepSeek V4

Meta

OpenAI

Anthropic