GLM-4.7
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Deploying GLM-4.7 with Claude-Compatible API
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Experimenting with GLM-4.7 for internal tools and workflows led to deploying it behind a Claude-compatible API, offering a cost-effective alternative for tasks like agent experiments and code-related activities. While official APIs are stable, their high costs for continuous testing prompted the exploration of self-hosting, which proved cumbersome due to GPU management demands. The current setup with GLM-4.7 provides strong performance for code and reasoning tasks, with significant cost savings and easy integration due to the Claude-style request/response format. However, stability relies heavily on GPU scheduling, and this approach isn't a complete replacement for Claude, especially where output consistency and safety are critical. This matters because it highlights a viable, cost-effective solution for those needing flexibility and scalability in AI model deployment without the high costs of official APIs.
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GLM 4.7: A Solid Choice for Coding Projects
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GLM 4.7 has shown strong performance in coding tasks such as refactoring, debugging, and code review, particularly excelling in Python backend work by maintaining context and catching logic issues. It compares favorably to Deepseek v3 by slightly better maintaining context in long conversations, though it struggles with complex algorithmic tasks. In comparison to Qwen2.5-coder, GLM is more consistent in maintaining conversation flow, while being less verbose than Kimi. Although it struggles with complex React state management and architectural decisions, its open-source nature and cost-effectiveness make it a viable option for developers focused on implementation tasks. This matters because choosing the right coding model can significantly impact productivity and cost efficiency in software development workflows.
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Optimizing GLM-4.7 on 2015 CPU-Only Hardware
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Running the massive 355B parameter GLM-4.7 Mixture of Experts model on a 2015 Lenovo System x3950 X6 with eight Xeon E7-8880 v3 CPUs showcases the potential of older hardware for local large language models. By using Q8_0 quantization, the model maintains high-quality outputs with minimal degradation, achieving around 5-6 tokens per second without a GPU. Key optimizations include BIOS tweaks, NUMA node distribution, llama.cpp forks for MoE architecture, and Linux kernel adjustments, although the setup is power-intensive, drawing about 1300W AC. This approach is ideal for homelab enthusiasts or those lacking modern GPUs, offering a viable solution for running large models locally. This matters because it demonstrates how older hardware can still be leveraged effectively for advanced AI tasks, expanding access to powerful models without the need for cutting-edge technology.
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Join the AMA with Z.ai on GLM-4.7
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Z.ai, the open-source lab renowned for its development of GLM-4.7, is hosting an Ask Me Anything (AMA) session. This event is scheduled for Tuesday from 8 AM to 11 AM PST, and it provides a unique opportunity for enthusiasts and professionals to engage directly with the creators. The session is designed to foster open dialogue and transparency, allowing participants to inquire about the intricacies of GLM-4.7 and the broader objectives of Z.ai. GLM-4.7 is a significant advancement in the field of machine learning, offering enhanced capabilities and performance. The model is part of a growing trend towards open-source AI development, which encourages collaboration and innovation by making cutting-edge technology accessible to a wider audience. This AMA session is an invitation for the community to delve deeper into the technical aspects and potential applications of GLM-4.7, as well as to understand the motivations and future plans of Z.ai. Engagement in this AMA is open to everyone, allowing for a diverse range of questions and discussions. This inclusivity is essential for driving the evolution of AI technologies, as it brings together varied perspectives and expertise. By participating, individuals can contribute to the collective knowledge and development of open-source AI, which is crucial for ensuring that advancements in technology are shared and utilized for the benefit of all. This matters because open-source initiatives like this democratize access to AI, fostering innovation and collaboration on a global scale.