Why do we need to introduce this series?
Llama is Meta's model series. On April 5, 2025, Llama 4 Scout and Maverick were officially introduced, emphasizing the multi-modal capabilities of native graphics and text and open access. This article uses it as a representative case for understanding open weight deployment, and does not write the earlier released model as the new model just launched today.
How to distinguish between Scout and Maverick
Both are expert hybrid architectures, and the officially announced total size and number of experts are different. For users, what is more important are actual operating conditions, contextual requirements and task performance. The so-called activation parameters are not a simple equivalent of the memory required to load the model. You cannot decide what device to buy just by looking at a small number.
Application suitable for research (ChinaOPC analysis)
Teams with technical skills who want to control the deployment environment can research internal material Q&A or graphic content classification. First select non-sensitive samples, confirm the data extraction and answering methods, and then evaluate whether customization is needed. If you only generate copy occasionally, the investment in maintaining a complete set of inference services may be higher than using a hosted portal.
For example, when classifying a product catalog, the model can be used to suggest labels based on images and parameters, and then the person in charge can check for misclassified items. The fact that the pictures look similar does not prove that the accessories are compatible. The applicable conditions in the catalog should still be based on the manufacturer's information. The purpose of classification tools is to assist in sorting, not to replace technical confirmation.
What work is required for self-deployment?
In addition to model files, operational frameworks, capacity planning, access control, logging, backups, and version updates are also required. Quantification can change resource requirements and may also change output performance, so the final actual deployed version should be tested. Multi-user concurrency and long inputs tend to be closer to the true cost than a single demo.
Open weight does not mean no conditions for use
The download portal, model card, and community license should be read together to confirm that the commercial use and distribution conditions are suitable for your business. This article does not judge licensing eligibility for a specific project, nor does it promise that any hardware combination will run smoothly. Before going online, complete a recordable acceptance using business samples and recovery processes.
Continue to compare
Compare Gemma’s local multimodality; Understand Mistral’s open weight direction. You can also browse the AI model column.