# Jit-Roy/WeeLLM

<https://repo.masters-masters.com/en/repos/Jit-Roy/WeeLLM>

[Home](https://repo.masters-masters.com/en) › [Jit-Roy](https://repo.masters-masters.com/en/owners/Jit-Roy) › [WeeLLM](https://repo.masters-masters.com/en/repos/Jit-Roy/WeeLLM)

Jit-Roy/WeeLLM has 144 stars on GitHub. Its RepoMasters card: Prototype tier. WeeLLM runs large diffusion models with as little as 4 GB of VRAM, without any…

| Field | Value |
| --- | --- |
| Owner | [Jit-Roy](https://repo.masters-masters.com/en/owners/Jit-Roy) |
| Stars | 144 |
| Forks | 13 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | None |
| Created | 2026 |
| Topics | flux, low-vram, stable-diffusion, z-image, hidream, krea2, qwen-image, qwen-image-edit, sdxl, diffusion |
| Tier | [Prototype (100 to 249 ★)](https://repo.masters-masters.com/en/ranks/rush) |
| View on GitHub | [Jit-Roy/WeeLLM](https://github.com/Jit-Roy/WeeLLM) |

## Repository

WeeLLM runs large diffusion models with as little as 4 GB of VRAM, without any quantization. It dynamically determines how many layers can fit within the available VRAM and streams the text encoder and transformer layers to the GPU layer by layer, enabling inference on hardware with limited VRAM. I…

The tier for repos with 100 to 249 ★. Published odds per card pulled: 70%. Rarity: Common.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.