Paiton

Run chat, coding, image and video models on AMD Radeon with Paiton's native GPU runtimes, integrated with vLLM, Diffusers and ComfyUI.

Using Qwen3.8? Start with its MXFP4 or 3-bit quickstart. Download the weights, then pick how to run it: MXFP4 for the highest accuracy, 3-bit for speed, --mode long for one long document, --mode long-kv4 for coding agents (prefix caching, about 570K tokens of cache), opt-in --mode long-512k for up to 524K, --vision for images.

Prefer a desktop app? Paiton Studio provides chat, image and video tools for the supported models.

Models · Quick start · Requirements · Your own vLLM · Docs

Quick start

The container launchers include each model's supported inference environment. Choose a model below and follow its guide. For the smallest language model, MiniCPM5-2B:

git clone --depth 1 https://github.com/Eliovp-BV/paiton-vllm-plugin.git
cd paiton-vllm-plugin
./models/MiniCPM5-2B/serve-docker.sh

The first launch downloads its weights and prepares runtime components; later launches reuse the caches. Its guide includes chat and API examples. Qwen3.8 and Meeting need weight preparation before launch. Run one model at a time on the tested single-GPU setup.

Models

Model links lead to setup, launch options and support limits. Benchmark links include the tested settings and quality tradeoffs; results from different models or profiles are separate comparisons.

Chat, reasoning and coding

These models serve an OpenAI-compatible API through vLLM.

Image generation

Video generation

Meeting notes

Meeting is a review candidate for transcripts, speaker labels and timestamped notes from recordings. Notes need review against the recording; live Teams capture and Studio integration are unsupported. Benchmark.

Use your own vLLM environment

The pip-installable plugin can run supported presets inside an existing matching vLLM build. It checks the checkpoint, runtime and native artifacts; it does not install or replace vLLM, PyTorch or ROCm.

Activate the preset's supported environment, then:

python -m pip install https://github.com/Eliovp-BV/paiton-vllm-plugin/releases/download/v0.3.4/paiton_vllm_plugin-0.3.4-py3-none-any.whl
paiton doctor
paiton serve minicpm5

See native execution for all presets, exact runtime requirements, local weights and offline use. The native qwen38-nvfp4 preset runs without DFlash2 or the 3-bit weights; use the Qwen3.8 container guide for those release profiles and their benchmark results.

Model weights and existing downloads

Cloning this repository gets the launchers and guides. Follow your model's guide to download its pinned weights or reuse an existing copy. See weights and caches for local folders and Docker mounts.

Requirements

• Tested GPU: one Radeon AI PRO R9700, 32 GB, RDNA4 / gfx1201. Other GPUs have not been qualified.
• Containers: Linux, Docker and AMD device access through /dev/kfd and /dev/dri; ComfyUI launchers also need Docker Compose.
• Native plugin: the preset's exact supported environment, listed in native execution.
• Memory and storage: depend on the model, context, concurrency and image/video settings; check the model guide before downloading.

Where to find things

About Paiton

This repository contains public integrations and compiled runtime artifacts. The compiler is proprietary. The plugin is Apache-2.0 licensed; model weights and bundled components retain their own licenses. See third-party notices and each model's notices.

More about Paiton.