I'm evaluating whether to use Ollama for our next Robotic Process Automation (RPA) project. We need to bridge the gap between AI reasoning and legacy desktop applications. Does Ollama provide enough speed for complex sequences, or is the overhead of local inference too high for real-time bots? I've heard that without an H100, the response time can lag in a production loop.
3 answers
In Robotic Process Automation, the struggle has always been the "brittleness" of scripts. Using Ollama allows us to add a layer of semantic understanding without the 500ms network round-trip of a cloud API. For a simple check or data extraction task, a small 3B or 7B model running on a local workstation is often faster than the cloud because of that lack of latency. However, for a sequence that requires 10+ steps of reasoning, you really need a dedicated GPU like an RTX 4090 to keep the "bot" feeling responsive. If it takes 10 seconds to decide the next click, your RPA efficiency takes a massive hit.
If Ollama is running on a shared server, how do we handle priority when multiple bots are asking for inference at once?
We use it for the "offline" parts of our flow, then call the cloud for the complex decision nodes.
That's a solid hybrid approach, Diana. Balancing the local speed with cloud intelligence is key for modern Robotic Process Automation architectures.
Justin, that's where the "Ollama as a service" model gets tricky. Unlike cloud providers that scale horizontally for you, a local server is limited by its VRAM. We usually implement a simple Project Management queue in our RPA orchestrator. This ensures that a mission-critical bot gets the GPU "slot" first. If you have 50 bots all hitting one Ollama instance, you’ll see massive slowdowns. For high-scale automation, we actually deploy one small Ollama container per bot instance, effectively giving each worker its own dedicated slice of the local GPU.