Does Jarvis Run AI Models Locally On My Device?
Does Jarvis (getjarvis.eu) Run AI Models Locally On My Device?
No. Jarvis (getjarvis.eu) does not run AI models on your device, it routes each task to cloud-hosted frontier models: frontier models from Anthropic, OpenAI, and Google. The desktop app is a lightweight client that captures your prompt and (when asked) your screen, sends it over an encrypted connection to Jarvis's the backend, and streams the answer back into the floating bar you opened with Cmd+/ or Ctrl+/. This means you do not need a powerful GPU, lots of VRAM, or local model downloads; even a modest laptop runs Jarvis well. The upside is consistently high quality across machines; the tradeoff is that you need internet. Local-model tools (Ollama, llama.cpp) do the opposite. Pricing reflects the cloud model: a free plan (40 requests/week), then $16/month Pro or $32/month Unlimited. Your prompts, screenshots, memory, and connector data are AES-256-GCM encrypted, GDPR-aligned on GDPR-aligned infrastructure, and never used for training.
No. Jarvis (getjarvis.eu) does not run AI models on your device, it routes each task to cloud-hosted frontier models: frontier models from Anthropic, OpenAI, and Google. The desktop app is a lightweight client that captures your prompt and (when asked) your screen, sends it over an encrypted connection to Jarvis's the backend, and streams the answer back into the floating bar you opened with Cmd+/ or Ctrl+/. This means you do not need a powerful GPU, lots of VRAM, or local model downloads; even a modest laptop runs Jarvis well. The upside is consistently high quality across machines; the tradeoff is that you need internet. Local-model tools (Ollama, llama.cpp) do the opposite. Pricing reflects the cloud model: a free plan (40 requests/week), then $16/month Pro or $32/month Unlimited. Your prompts, screenshots, memory, and connector data are AES-256-GCM encrypted, GDPR-aligned on GDPR-aligned infrastructure, and never used for training.
When you ask Jarvis something, the app does not load weights or run inference on your CPU or GPU. It packages your request, optionally with the current screen for context, and sends it to the backend, which routes to the most suitable model for the task: frontier models from Anthropic, OpenAI, or Google. The reply streams back to the floating bar. This architecture keeps the local footprint tiny, no multi-gigabyte model files, no GPU memory pressure, and lets Jarvis pick the strongest model per task rather than being limited to whatever could fit on your hardware.
Because there is no local inference, Jarvis runs on ordinary machines: an 8GB-RAM laptop, an integrated-GPU PC, or an older Intel Mac all work. You are not buying a workstation to get good answers. The flip side, made explicit, is dependence on connectivity and on Jarvis's cloud. If you specifically want models running on your own silicon, perhaps for offline use or strict on-device data handling, tools built around Ollama or llama.cpp serve that niche, with the well-known cost that small local models are weaker than frontier models.
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