Understanding the Open AI Stack
The emergence of open-source AI models is reshaping development workflows. This post details a stack designed for developers seeking greater control and economics, moving away from closed-source models. The core components – model, inference, gateways, and harness – provide a familiar framework, mirroring the approach used with established closed-source solutions. This allows for rapid experimentation and adaptation to new model releases.
Model Layer Details
At the heart of the stack lies the model itself. These models, often Mixture-of-Experts (MoE) architectures, predict the next tokens based on training data. Larger models, such as Kimi K3 (1.8T total parameters, 104B active), demonstrate enhanced reasoning and performance on complex tasks. These models are particularly useful in agent-style workflows where the model needs to autonomously determine the next step.
Comparing Model Sizes
Model size is a critical factor. GLM 5.3 Flash (320B total parameters, 18B active) offers a significantly smaller and more cost-effective alternative. The difference isn't necessarily about quality, but about the ability to handle ambiguity. Small models excel when tasks are clearly defined and well-scoped, executing instructions precisely without needing to infer or explore multiple interpretations.
Performance and Cost Considerations
Small models like GLM 5.3 Flash are substantially faster and cheaper than larger models like Kimi K3. This difference is particularly impactful in agentic workflows, where rapid iteration and experimentation are key. The ability to switch models in minutes, rather than rebuilding workflows, unlocks the potential to quickly evaluate new model releases and optimize performance.
Source: https://www.together.ai/blog/the-open-source-ai-stack
