I ran Qwen3.8-27B locally on an RTX 5060 Ti 16GB (github.com)

A new deployment package has been successfully created to run the quantized Qwen3.8 27B coding agent locally on an NVIDIA RTX 5060 Ti with 16GB VRAM, leveraging llama.cpp and a quantized KV cache. This package allows for compatibility with OpenAI clients like Codex and Pi, as well as an adapter for Claude Code, enabling a broad scope of applications. The installation is designed for efficiency, focusing on whether the agent can manage complex tasks such as reading design documents, modifying code, and passing tests, while minimizing memory overflow and optimizing performance. This development is significant for the AI/ML community as it demonstrates a practical approach to deploying large language models in constrained environments, highlighting the balance between CPU and GPU resources. Performance tests showed that using a component configuration with some feedforward network (FFN) layers offloaded to the CPU not only alleviated GPU memory pressure but also improved stability during longer contexts. For instance, the agent completed tasks more efficiently with a measured speed of 656.86 tokens per second with a specific CPU offload strategy. This experiment emphasizes the importance of system architecture in optimizing AI model deployment, potentially guiding future developments and experiments in local machine learning applications.