Local model runtime
Local model serving and task-routing infrastructure. Supports different model services for coding and fast-response tasks, with operational and behavior checks.
Inside the application
Selected use cases, source information and operational outputs.
Coding and short-response tasks can be routed to different model services.
Model, tool and network policy
Apple Silicon deployments support MLX, with GGUF and llama.cpp options when needed.
Model, tool and network policy
Service health, tool use and rule adherence are checked.
Dimensions to evaluate
No result dataShows comparison criteria. Numerical charts require verified data and a defined method.
Model, tool and network policy
Application approach
The scope, operating approach and boundaries of this work.
Coding and short-response tasks can be routed to different model services. Applications access model selection through a shared service interface.
Apple Silicon deployments support MLX, with GGUF and llama.cpp options when needed. External providers are configured separately according to requirements and data policy.
Service health, tool use and rule adherence are checked. Model changes are evaluated against the same existing tasks.
In everyday work
Running locally involves more than downloading a model. Hardware capacity, task type, tools and external connectivity need joint assessment. Yerel addresses this operating environment.
Choosing tasks, models and hardware together
MLX on Apple Silicon and, where needed, GGUF / llama.cpp options are considered. External providers follow explicit operational and data requirements.
This component shows incoming information together with its identity and provenance. Coding and quick-response tasks can be routed to appropriate model services. Applications use a common service interface for model selection.
This component explains how records are reviewed rather than displaying a result in isolation. MLX on Apple Silicon and, where needed, GGUF / llama.cpp options are considered. External providers follow explicit operational and data requirements.
This component distinguishes the user’s decision from the next work record. Service health and tool behavior are checked against comparable tasks. Local processing does not imply that every channel and integration is offline.
The whole operation
These distinctions show the information the application receives, where it needs the user and what it leaves for the next operation.
These are evaluation dimensions, not measured performance results. Comparisons use the same task types, data scope and human-review conditions.
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