The excitement around running LLMs locally — on laptops, edge devices, even phones — suggests a path to private AI automation. But running a local model is not the same as solving the automation problem. Intelligence is necessary but insufficient.
Why Local LLM ≠ Autonomy
A local LLM can understand a screenshot. It can describe what's on screen, identify elements, even suggest what to click next. This is impressive capability.
But understanding a screen is not the same as controlling it. The LLM has no access to mouse, keyboard, or system APIs. It can't execute its own suggestions.
Execution requires additional infrastructure: screen capture, coordinate translation, input injection, result verification, state tracking, error recovery. None of this comes with a local LLM.
Running Llama locally gives you a brain. You still need eyes, hands, and a nervous system.
Where Local Models Help
Local models excel at privacy-preserving perception. Visual understanding without sending screens externally. Text extraction from images. Element classification. Layout analysis.
They're also valuable for low-latency decisions. When the agent needs to decide quickly whether to retry or fail, a local model can respond in milliseconds rather than the seconds required for cloud round-trips.
Local models handle routine classification well. Is this a login screen? Is the operation complete? Did an error appear? These constrained decisions work reliably on smaller models.
The pattern: local models for perception and routine decisions, not for complex multi-step planning.
Where Cloud Reasoning Still Wins
Complex task decomposition benefits from larger models. Breaking "process this insurance claim" into 47 steps across 6 applications is a hard planning problem that smaller local models struggle with.
Novel situation handling — cases the system hasn't seen before — benefits from the broader training of larger models. Local models can't match cloud model generalization.
Multi-step reasoning with long context windows is computationally expensive. Cloud models handle longer contexts with better coherence than current local alternatives.
The frontier of AI capability is in the cloud. Local models are typically 6-18 months behind. For pushing capability boundaries, cloud access matters.
The Hybrid Necessity
Neither local-only nor cloud-only architectures work for enterprise automation. Local-only sacrifices capability. Cloud-only violates privacy constraints.
The viable architecture is hybrid: local processing for sensitive perception and execution, cloud processing for complex planning on sanitized abstractions.
This hybrid model isn't a compromise — it's an optimization. Use each resource for what it's good at. Local for privacy and latency. Cloud for capability and generalization.
The engineering challenge is the boundary: what crosses from local to cloud, what doesn't, and how to maintain coherent operation across the split.
Key Takeaway
Local LLMs solve the privacy problem for perception but not the automation problem. Execution requires infrastructure beyond the model. Complex planning benefits from cloud capability. The answer is hybrid architecture, not local-only ideology.