The video reviews Ollama’s new “Launch” feature, which enables easy local deployment of coding models like Claude Code using the efficient GLM 4.7 Flash model, now with Anthropic API support. While setup is straightforward and the model can perform many coding tasks, local performance is much slower and less reliable than cloud-based solutions, making it impractical for most users without high-end hardware.
The video discusses recent updates to Ollama, specifically its new support for the Anthropic API and the introduction of the “Ollama Launch” feature. The creator focuses on testing the new GLM 4.7 Flash model, a smaller, more efficient version of the GLM 4.7 model, which is comparable in size to the Qwen Mixture of Experts models. This model has 30 billion parameters, with 3 billion active at any time, making it feasible to run locally on a Mac. The developers behind GLM, Zhipu AI, are promoting this model for use with Claude Code, and the smaller “Flash” version is designed to be more accessible for local deployment.
Ollama Launch is introduced as a straightforward way to run Claude Code and other coding models like CodeX, Droid, or OpenCode with Anthropic API support. The process is user-friendly: update Ollama to the latest version, download the desired model (in this case, GLM 4.7 Flash), and use a simple command to launch the application. The video highlights the importance of adjusting the context length from the default 4,096 tokens to 64,000 tokens in the app settings to ensure the model can effectively utilize its context window and perform tasks properly.
The creator demonstrates launching Claude Code with the GLM 4.7 Flash model and notes that, while the setup is easy, running the model locally is significantly slower than using Anthropic’s cloud-based Claude models like Opus. Both the initial response (prefill) and the decoding process take more time due to the limitations of local hardware, especially when handling large context windows.
After about 90 minutes of testing on a Mac Mini Pro with 32GB of memory, the creator observes that the model can perform many Claude Code functions, such as calling MCP tools. However, there are occasional issues, like incorrect tool arguments, which are less common with cloud-based models like Opus 4.5. These problems may stem from the quantization of the model and the large context window. The creator concludes that, while running Claude Code locally is possible, it is not yet a practical alternative for most users unless they have a very powerful machine.
Despite these limitations, the creator remains optimistic about the future of local coding models, especially as new models like Gemma and Qwen 4 are released. Ollama Launch is praised for its simplicity and potential, but it is not yet ready for mainstream use unless users have high-end hardware. The video ends with an invitation for viewers to try the setup themselves, share their experiences, and look forward to further improvements in local AI coding models.