CouncilAI runs 4 AI models on your own hardware — completely offline, no data ever leaves your device. It routes questions by difficulty: simple ones go to a fast model, complex ones trigger a full deliberation where models compete and the strongest answer wins. v1.2 adds Sequential Mode (draft → critique → refine), Compare Mode, and a transparency panel showing exactly how every routing decision was made. Beta: $3 USD — councilai.ca
Hey Product Hunt! I'm the solo dev behind CouncilAI.
I built this because I wanted local AI that actually shows its reasoning — not just a chat box that spits out answers. Every response shows which model was chosen, why, and how confident the router was.
v1.2 just shipped with Sequential Mode (three models working in sequence to refine an answer), Compare Mode for side-by-side model analysis, and a full transparency panel.
Everything runs on your own hardware. No cloud, no accounts, no data leaving your machine — ever.
Happy to answer any questions about the routing logic, the 4-model architecture, or anything else. Would love feedback from this community!
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genuinely curious — what hardware are you realistically targeting for running all four models at once? even a decent quantized mix would chew through ram on most consumer machines, so wondering if there's a minimum spec you'd recommend
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Maker
@zeliha8u0s Great question. CouncilAI doesn't run all four models simultaneously — that's an important distinction. The router (Llama3.2:1b) classifies the question, then a single responder model is loaded for that query. So at any given time you're running at most two models: the router and one responder.
In Council Mode all three responders do run, but sequentially via Ollama's model loading — not simultaneously in VRAM. Ollama handles the loading/unloading between them.
Minimum recommended specs:
8GB RAM — can run the full stack with quantized models (Gemma2:2b + Phi3:mini + Mistral q4)
16GB RAM — comfortable, no swapping issues
Dedicated GPU with 6GB+ VRAM — significantly faster responses
The hardware detection on first launch adjusts model selection to your specs — lower RAM machines get smaller quantizations automatically. On a 8GB machine with no GPU responses are slower (30-90 seconds for Mistral) but it does work.
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genuinely curious — what hardware are you realistically targeting for running all four models at once? even a decent quantized mix would chew through ram on most consumer machines, so wondering if there's a minimum spec you'd recommend
@zeliha8u0s Great question. CouncilAI doesn't run all four models simultaneously — that's an important distinction. The router (Llama3.2:1b) classifies the question, then a single responder model is loaded for that query. So at any given time you're running at most two models: the router and one responder.
In Council Mode all three responders do run, but sequentially via Ollama's model loading — not simultaneously in VRAM. Ollama handles the loading/unloading between them.
Minimum recommended specs:
8GB RAM — can run the full stack with quantized models (Gemma2:2b + Phi3:mini + Mistral q4)
16GB RAM — comfortable, no swapping issues
Dedicated GPU with 6GB+ VRAM — significantly faster responses
The hardware detection on first launch adjusts model selection to your specs — lower RAM machines get smaller quantizations automatically. On a 8GB machine with no GPU responses are slower (30-90 seconds for Mistral) but it does work.