What's in your current AI stack, and how does it compare to mine with Ollama and Hermes?
agents gemma llama qwen
| Source: Mastodon | Original article
Local AI stacks vary, with some using Ollama and Hermes. AI setups are being tested with different models.
The local AI stack is becoming increasingly important for developers, with many opting for a blend of different models to drive their agents. As we see in a recent example, a developer is using #Ollama and #Hermes with a combination of `gemma4:e4b-mlx` and `qwen3.5:9b-mlx` to power their work in Zed for Python development.
This setup allows for efficient performance, with the developer reporting average core temperatures staying just under 90°C. The use of local AI stacks like this is significant because it enables developers to work offline and maintain control over their data, a trend that has been gaining traction in recent months.
What to watch next is how these local AI stacks evolve, with new models like `ornith:9b` being considered for testing. As the field continues to advance, it will be interesting to see how developers balance performance and temperature management in their setups.
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