llama.cpp is featured in the Github Octoverse report The project remains consistently one of the top open-source projects on Github over the last few years. An attestation to it's great quality and awesome community!

It is very cool to see llama.cpp on the big stage in todays Windows event! The software and hardware stacks are finally coming together. Our community has put a lot of hard work in the past years and it shows. Looking forward to more users embracing local AI.

The new v0.6.0 release packs a lot of good stuff: - Clef support (text + vision) - High-quality support for Qwen3.8-Flash-Next - Massive performance improvement with Metal - New `llama_batch_ext` API Also the website got a nice refresh:

Decision models in llama.cpp are now available The `/v1/systemone` endpoint is available in the latest llama builds. Use it to do Jev-style inference locally, efficiently and privately. Multiple open models are supported with more to come.

You don't need to be a command-line expert to run local AI. wraps the heavy lifting of llama.cpp into a clean UI, making it simple to deploy and manage models like Gemma 4. Instead of dealing with config files, you get one-click model downloads, clear memory estimates, and no coding required! Watch the video to see Gemma 4 in action as it parses tables from receipts, streams reasoning logs, and connects to MCP for web search.

Just made a quick tutorial on running local models in Pi with llama.cpp step by step 0:00 Why local models in Pi 0:59 Install llama.cpp 1:49 Choose and load a model Written version in 🧵

Hugging Face has been acquired by NVIDIA It is quite exciting to be a part of this journey! NVIDIA has been an active supporter of the llama.cpp project. For more than a year now, their engineers have actively contributed to the codebase, collaborated with the community and provisioned hardware for development and testing purposes. The local AI ecosystem has largely benefited from our joint efforts. Going forward, llama.cpp/ggml will stick to its founding principles. One of the most important qualities of the project is to be hardware-agnostic. Therefore the development and support of all backends will continue to be done as usual - driven and shaped by the community. Open to everyone who is willing to participate. The existence of such an independent software platform is crucial for the rapid adoption of AI locally and for bringing it closer to the user. Now, with such a significant partner as NVIDIA supporting us, in addition to the wider Hugging Face team, I believe that our long-term goals will be easier to achieve. We will remain focused on building one of the most exciting projects based on the most exciting technology of our lifetime and making it truly available to everyone in the most accessible and efficient way possible.

Super happy to officially announce that we are a̶c̶q̶u̶i̶r̶i̶n̶g̶ joining forces with @nvidia 🔥 Here is a more personal take: AI is at an inflection point. Open source AI can become less relevant in the coming years if the big closed labs run away with it, OR it can become the foundational fabric of the next phase of human civilization. Those are vastly different outcomes, and we need the critical mass to ensure we give our collective best shot to the second outcome. Given @JensenHuang's stance on open source AI and how he stepped up to defend it when it was under threat earlier in the summer, NVIDIA was the only partner we truly considered. HF will remain an independently run, neutral platform. This gives fuel to our long-term vision and mission of unlocking the community's progress to ensure that AI, which is the greatest breakthrough of our lifetime, is accessible to as many people as possible.

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llama.cpp docs now have a new home, shipped with ❤️ upcoming: speculative decoding in detail, quantization (k-quant, i-quant), coding agents what do you want to see next? 🤗 📖 @llama_cpp

I find the "inception" pattern to be very useful in many agentic use cases. You can force the model to take an action when it thinks for too long by injecting a thought after a specified reasoning budget. Helps dealing with underspecified tasks which make the model reason for way too long.