Hardware & Infra: the chips, GPUs and data centres behind AI

Chips, devices and the infrastructure AI runs on.

Why is RAM so expensive right now, and when will prices come down?

Kelly B.

  1. What does '2nm' actually mean? TSMC N2 and nanosheet transistors, explained

    Kelly B.

  2. How to run vLLM as an OpenAI-compatible server (and swap it into your app)

    Kelly B.

Latest in Hardware & Infra

20 stories

  1. Unified memory explained: Mac Studio vs RTX Spark vs a desktop GPU for local AI

    Kelly B.

  2. TPU vs GPU: what's the difference, and which one runs your AI?

    Kelly B.

  3. SSD and NAND prices in Q4 2026: why AI is now eating storage too

    Kelly B.

  4. Can you run an AI model bigger than your RAM? mmap and offloading, explained

    Kelly B.

  5. NVIDIA RTX Spark laptops: 128GB of unified memory on Windows, explained

    Kelly B.

  6. Why the RTX 5090 costs several times its $1,999 launch price right now

    Kelly B.

  7. PS6 and the memory crunch: why AI demand could push next-gen console prices up

    Kelly B.

  8. NVIDIA DSX POD explained: AI factories in a container, from six months to 14 days

    Kelly B.

  9. Mac Studio M5 Ultra with 512GB: what it means for running big AI models at home

    Kelly B.

  10. How much VRAM do you need to run an LLM locally? The simple math

    Kelly B.

Hardware & Infra desk

Chips, devices and the infrastructure AI runs on.

Editor's picks

  1. What is MCP? The Model Context Protocol explained for vibe codersBuildCodema Editorial ·
  2. What are agent skills? How to write your first SKILL.mdAICodema Editorial ·
  3. Git and GitHub basics for vibe coders: never lose AI-written codeBuildCodema Editorial ·
  4. How much VRAM do you need to run an LLM locally? The simple mathHardware & InfraKelly B. ·
  5. What is an agent harness? Google's Harness² shows why it matters as much as the modelAIJohn F. ·

Codema Daily

Codema Daily

A short daily note on AI, building, hardware and AI money.

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