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sdkit

Post from Oct 27, 2025

A possible intuition for understanding GPU memory hierarchy (and the performance penalty for data transfer between various layers) is to think of it like a manufacturing logistics problem:

  1. CPU (host) to GPU (device) is like travelling overnight between two cities. The CPU city is like the "headquarters", and contains a mega-sized warehouse of parts (think football field sizes), also known as 'Host memory'.
  2. Each GPU is like a different city, containing its own warehouse outside the city, also known as 'Global Memory'. This warehouse stockpiles whatever it needs from the headquarters city (CPU).
  3. Each SM/Core/Tile is a factory located in different areas of the city. Each factory contains a small warehouse for stockpiling whatever inventory it needs, also known as 'Shared Memory'.
  4. Each warp is a bulk stamping machine inside the factory, producing 32 items in one shot. There's a tray next to each machine, also known as 'Registers'. This tray is used for keeping stuff temporarily for each stamping process.

Post from Oct 10, 2025

The design constraints for Easy Diffusion's next engine (i.e. sdkit v3) are:

  • Lean: Install size of < 200 MB uncompressed (excluding models).
  • Fast: Performance within 10% of the best-possible speed on that GPU for that model.
  • Capable: Supports Stable Diffusion 1.x, 2.x, 3.x, XL, Flux, Chroma, ControlNet, LORA, Embedding, VAE. Supports loading custom model weights (from civitai etc), and memory offloading (for smaller GPUs).
  • Targets: Desktops and Laptops, Windows/Linux/Mac, NVIDIA/AMD/Intel/Apple.