github.com/FastFlowLM/FastFlowLM @v0.9.44

⚡ FastFlowLM (FLM) — Unlock Ryzen™ AI NPUs

Run large language models — now with Vision, Audio, Embedding and MoE support — on AMD Ryzen™ AI NPUs in minutes.
No GPU required. Faster and over 10× more power-efficient. Supports context lengths up to 256k tokens. Ultra-Lightweight (17 MB). Installs within 20 seconds.

📦 The only out-of-box, NPU-first runtime built exclusively for Ryzen™ AI.
🤝 Think Ollama — but deeply optimized for NPUs.
✨ From Idle Silicon to Instant Power — FastFlowLM Makes Ryzen™ AI Shine.

FastFlowLM (FLM) supports all Ryzen™ AI Series chips with XDNA2 NPUs (Strix, Strix Halo, Kraken, and Gorgon Point).

🔗 Quick Links

🔽 Download | 📊 Benchmarks | 📦 Model List

🐧 Linux Getting Started Guide

📖 Docs | 📺 Demos | 🧪 Test Drive | 💬 Discord

🚀 Quick Start

A packaged FLM Windows installer is available here: flm-setup.exe. For more details, see the release notes.

📺 Watch the quick start video (Windows)

[!IMPORTANT]
⚠️ Ensure NPU driver version is >= 32.0.203.304 (.304 is the minimum requirement but .311 is recommended; check via Task Manager→Performance→NPU or Device Manager).
⚙️ Tip: * RECOMMENDED: Try running Windows Update or Driver Download. * Official AMD Install Doc (AMD account required). * Unofficial forum downloads (CAUTION, we do not hold responsible for what you download here).

After installation, open PowerShell (Win + X → I). To run a model in terminal (CLI Mode):

flm run llama3.2:1b

Notes: - Internet access to HuggingFace is required to download the optimized model kernels. - Sometimes downloads from HuggingFace may get corrupted. If this happens, run flm pull <model_tag> --force (e.g. flm pull llama3.2:1b --force) to re-download and fix them. - By default, models are stored in: - Windows: C:\Users\<USER>\Documents\flm\models\ - Linux: ~/.config/flm/ - During installation on Windows, you can select a different base folder (e.g., if you choose C:\Users\<USER>\flm, models will be saved under C:\Users\<USER>\flm\models\). - On Linux, you can override the default location by setting the FLM_MODEL_PATH environment variable. - To disable the startup version check, set FLM_DISABLE_UPDATE_CHECK=1. - ⚠️ If HuggingFace is not accessible in your region, manually download the model (check this issue) and place it in the chosen directory.

🎉🚀 FastFlowLM (FLM) is ready — your NPU is unlocked and you can start chatting with models right away!

Open Task Manager (Ctrl + Shift + Esc). Go to the Performance tab → click NPU to monitor usage.

⚡ Quick Tips:
- Use /verbose during a session to turn on performance reporting (toggle off with /verbose again).
- Type /bye to exit a conversation.
- Run flm list in PowerShell to show all available models.

To start the local server (Server Mode):

flm serve llama3.2:1b

The model tag (e.g., llama3.2:1b) sets the initial model, which is optional. If another model is requested, FastFlowLM will automatically switch to it. Local server is on port 52625 (default).

📰 In the News

• 03/11/2026 🎉 FLM now supports Linux 🐧 ! To get started, check out the quick start guide or the Lemonade Server docs, and watch the short video for a quick walkthrough of FLM on Linux via Lemonade 🍋.

• 10/01/2025 🎉 FLM was integrated into AMD's Lemonade Server 🍋. Watch this short demo about using FLM in Lemonade.

🧠 Local AI on NPU

FLM makes it easy to run cutting-edge LLMs (and now VLMs) locally with: - ⚡ Fast and low power - 🧰 Simple CLI and API (REST and OpenAI API) - 🔐 Fully private and offline

No model rewrites, no tuning — it just works.

✅ Highlights

• Runs fully on AMD Ryzen™ AI NPU — no GPU or CPU load
• Lightweight runtime (17 MB) — installs within 20 seconds, easy to integrate
• Developer-first flow — like Ollama, but optimized for NPU
• Support for long context windows — up to 256k tokens (e.g., Qwen3-4B-Thinking-2507)
• No low-level tuning required — You focus on your app, we handle the rest

📄 License

• All orchestration code and CLI tools are open-source under the MIT License.
• These NPU-accelerated binary kernels are completely free for any use, including commercial use.
• Please acknowledge FastFlowLM in your README/project page (or product) as follows: Powered by [FastFlowLM](https://github.com/FastFlowLM/FastFlowLM)

💬 Have feedback/issues or want early access to our new releases? Open an issue or Join our Discord community

🙏 Acknowledgements

• Powered by the advanced AMD Ryzen™ AI NPU architecture
• Inspired by the widely adopted llama.cpp and Ollama
• Tokenization accelerated with MLC-ai/tokenizers-cpp
• Chat formatting via Google/minja
• Low-level kernels optimized using the powerful IRON+AIE-MLIR

🛠️ Building from Source

For developers who want to build FastFlowLM from source, we provide CMake presets for a convenient and consistent build experience.

Prerequisites

• Git
• CMake (version 3.22 or higher)
• A C++20 compatible compiler (e.g., GCC, Clang, MSVC)
• Ninja (recommended)

Build Instructions

More details on the exact procedure, with dependencies to be installed, for linux can be found in linux-getting-started.md.

1. Clone the repository:

   bash git clone --recursive https://github.com/FastFlowLM/FastFlowLM.git cd FastFlowLM/src

2. Configure CMake using presets:

   • For Linux:

     bash cmake --preset linux-default

     This will configure the build to install to /opt/fastflowlm.

   • For Windows (in a developer command prompt):

     bash cmake --preset windows-default

3. Build the project:

   bash cmake --build build

4. Install the project (optional):

   • For Linux:

     bash sudo cmake --install build

   • For Windows (with administrator privileges):

     bash cmake --install build