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Tag: Llama.cpp

Getting GLM 4.7 Working with Flash Attention on Llama.cpp

Introduction to GLM 4.7 and Llama.cpp

GLM 4.7 is a powerful language model that has been making waves in the AI community. To get the most out of this model, it’s essential to understand how to implement it using llama.cpp, a popular framework for running language models. In this article, we’ll explore how to get GLM 4.7 working with flash attention on llama.cpp, ensuring correct outputs and optimal performance.

Prerequisites and Setup

Before diving into the implementation, make sure you have the necessary prerequisites installed. This includes the latest version of llama.cpp, which can be obtained from the official GitHub repository. Additionally, you’ll need to download the GLM-4.7-Flash-GGUF model from Hugging Face.

Enabling Flash Attention on CUDA

To enable flash attention on CUDA, navigate to the glm_4.7_headsize branch of the llama.cpp repository. This branch contains the necessary modifications to support flash attention. Once you’ve checked out the branch, build the project using the provided instructions.

Running GLM 4.7 with Flash Attention

With the prerequisites and setup complete, you can now run GLM 4.7 with flash attention using the following command: export LLAMA_CACHE="unsloth/GLM-4.7-GGUF" && ./llama.cpp/llama-cli -hf unsloth/GLM-4.7-GGUF:UD-Q2_K_XL --jinja --ctx-size 16384 --flash-attn on --temp 1.0 --top-p 0.95 --fit on. This command sets the necessary environment variables, specifies the model and its parameters, and enables flash attention.

Troubleshooting Common Issues

When working with GLM 4.7 and llama.cpp, you may encounter issues such as slow inference speed or import errors with transformers. To address these problems, refer to the GLM-4.7-Flash Complete Guide, which provides detailed solutions and workarounds.

Conclusion and Future Implications

In conclusion, getting GLM 4.7 working with flash attention on llama.cpp requires careful attention to prerequisites, setup, and configuration. By following the steps outlined in this article and troubleshooting common issues, you can unlock the full potential of this powerful language model. As the field of AI continues to evolve, it’s essential to stay up-to-date with the latest developments and advancements in language models and their implementation.

January 21, 2026
Unlocking AI Potential with Kimi K2 Thinking

Introduction to Kimi K2 Thinking

Kimi K2 Thinking is a cutting-edge AI model that has been making waves in the tech community. Recently, a tester achieved an impressive 28.3 t/s on a 4x Mac Studio cluster, showcasing the model’s potential for high-performance computing.

Testing and Debugging

The tester was loaned a cluster of 4x Mac Studios (2x 512GB and 2x 256GB) by Apple until February. The initial testing phase was focused on debugging, as the RDMA support was still relatively new. However, now that the support is more stable, the tester can conduct more in-depth testing.

RDMA Tensor Setting and Llama.cpp RPC

The tester compared the performance of llama.cpp RPC and Exo’s new RDMA Tensor setting on the Mac Studio cluster. While the results are promising, the lack of a standardized benchmark like llama-bench in Exo makes direct comparisons challenging.

Smaller, More Efficient Models

The development of smaller, more efficient models is a key focus area in the AI community. These models can run on consumer hardware, making them more accessible to a wider audience. As Source 1 notes, ‘the future is smaller models’.

Hardware Advancements and RDMA

Advances in hardware, such as higher memory bandwidth and more RAM, are expected to make larger models more accessible on local hardware. The use of RDMA over Thunderbolt 5, as seen in Source 2, can significantly improve performance.

Running Kimi K2 Thinking Locally

For those interested in running Kimi K2 Thinking locally, Source 4 provides a step-by-step guide. The guide includes instructions on obtaining the latest llama.cpp and configuring the model for local use.

December 19, 2025
Ollama’s Enshittification: The Rise of Llama.cpp

Introduction to Ollama and Llama.cpp

Ollama, a popular tool for running large language models (LLMs) locally, has been making headlines with its recent changes. The project, which was initially open-source, has started to shift its focus towards becoming a profitable business, backed by Y Combinator (YC). This has led to concerns among users and developers about the potential enshittification of Ollama. Meanwhile, llama.cpp, an open-source framework that runs LLMs locally, has been gaining popularity as a free and easier-to-use alternative.

The Early Signs of Enshittification

According to Rost Glukhov’s article on Medium, Ollama’s enshittification is already visible. The platform’s recent updates have introduced a sign-in requirement for Turbo, a feature that was previously available without any restrictions. Additionally, some key features in the Mac app now depend on Ollama’s servers, raising concerns about the platform’s commitment to being a local-first experience.

Llama.cpp: The Open-Source Alternative

Llama.cpp, on the other hand, remains a free and open-source project. As noted by XDA Developers, llama.cpp is the base foundation for several popular GUIs, including LM Studio. By switching to llama.cpp, developers can integrate the framework directly into their scripts or use it as a backend for apps like chatbots.

Comparison of Ollama and Llama.cpp

A comparison of Ollama and llama.cpp by Picovoice.ai highlights the key differences between the two platforms. While Ollama aims to further optimize the performance and efficiency of llama.cpp, the latter remains a more straightforward and open-source solution. Llama.cpp’s compatibility with the original llama.cpp project also allows users to easily switch between the two implementations or integrate llama.cpp into their existing projects.

Conclusion and Future Implications

The rise of llama.cpp as a free and open-source alternative to Ollama has significant implications for the future of LLMs. As Ollama continues to prioritize profitability over open-source principles, users and developers may increasingly turn to llama.cpp for their local LLM needs. This shift could lead to a more decentralized and community-driven approach to AI development, with llama.cpp at the forefront.

November 19, 2025