vladimirrott/mojo-dllm
Library
Can I trust it?
No red flags in our checks.
Stars from real accounts No data
We haven't checked its stars for fakes yet.
Active OK
Last change yesterday.
Licence OK
Apache-2.0: you can use it for free, also in a commercial product. Keep the licence notice.
Quick checks from public data. They help, but they are no guarantee.
What it is
Local inference runtime for the diffusion language models LLaDA-8B and Dream-7B from quantized GGUF files, on CPU or NVIDIA GPU. The GGUF reader, tokenizer, quantized matrix multiply, GPU kernels, transformer and denoising loop are written in Mojo, with no Python at runtime.
Who it's for
Developers who want to run diffusion language models locally from GGUF files on CPU or NVIDIA GPU, without a Python runtime
How to use it
Ask your AI to set it up
Paste this into Claude, ChatGPT or your coding assistant.
Help me install and set up vladimirrott/mojo-dllm in my project. Follow its README: https://github.com/vladimirrott/mojo-dllm. Explain each step and check it is safe before running anything.
Star curve
Alternatives
Similar projects named in its profile. Each link searches the directory.
Get alerts for repos like this
We'll email you when something in Models & inference starts rising. Free, once a week, only what matches.
22,652 GitHub repos watched and ranked every hour. Figures as of 30 October 2026.
What arrives: one email on Mondays with the repos that match, never more. See last week's. Privacy
For maintainers
Add this badge to your README. It shows the repo's place on the radar, updates every hour and links back to this page.
[](https://repoalerts.com/repo/vladimirrott/mojo-dllm?ref=badge)Similar repos
- VecSzn/PolyStrata
Runs large open-weight language models locally on an NVIDIA GPU, with OpenAI- and Anthropic-compatible API endpoints served on localhost. Includes a second, experimental inference engine for several m
- NVIDIA/spark-local-inference
A local inference server for NVIDIA SM120/SM121 GPUs aimed at long conversations, coding agents and repeated tool use. Models are prepared at build time with PyTorch, Transformers and FlashInfer, then
- DiffuSpace/Clover-35B-A3B-Preview
A 35B-parameter mixture-of-experts diffusion language model built on Qwen3.6-35B-A3B, using block diffusion generation with a 128K context window. Inference is supported in Hugging Face Transformers,
- mw00/project-maya
Runs GLM-5.3-Flash and DeepSeek-V4.1-Flash-1017 locally on one or more NVIDIA or AMD GPUs, with an engine, server, browser dashboard, quantization tools and OpenAI- and Anthropic-compatible API. Exper
- ekzhang/resin
Experimental Python and Rust library of building blocks for LLM inference engines. Provides a headless Rust scheduler, KV cache and metrics server, where the user writes model-specific forward-pass ke
- jesdga95/basalt
A C++ local inference engine for the Qwen3.8-Flash-Next mixture-of-experts model on NVIDIA Blackwell GPUs (sm_120) under Linux and Windows. Experts that do not fit in VRAM are kept in system RAM, opti