This is now my favorite way to visualize these concepts in practice.
- Who wants to drive across town to inspect a €50 item for a small fee (we can draw comparison to Uber Eats like platforms fees economies)?
- Can a random broker validate a luxury watch? Do we need another blockchain tech for broker validator skill reputation?
- Physical validation adds days to trades, in online economy, the faster the merrier
- Fees might price out low-value items
Let's see how this plays out.
This is now my favorite way to visualize these concepts in practice.
Bonus 1: Use any combination of models. Mix n match models from any lab.
Bonus 2: Serve your custom consortium on a local API from a single command using the llm-model-gateway plugin and use it in your apps and coding assistants.
https://x.com/karpathy/status/1870692546969735361
> uv tool install llm
llm install llm-consortium
llm consortium save gthink-n5 -m gemini-pro -n 5 --arbiter gemini-flash --confidence-threshold 99 --max-iterations 4
llm serve --host 0.0.0.0
curl http://0.0.0.0:8000/v1/chat/completions \
-X POST \
-H "Content-Type: application/json" \
-d '{
"model": "gthink-n5",
"messages": [{"role": "user", "content": "find a polynomial algorithm for graph-isomorphism"}]
}'
You can also build a consortium of consortiums like so: llm consortium save gem-squared -m gthink-n5 -n 2 --arbiter gem-flash
Or even make the arbiter a consortium: llm consortium save gem-cubed -m gthink-n5 -n 2 --arbiter gthink-n5 --max-iteration 2
or go openweights only: llm consortium save open-council -m qwen3:2 -m kimi-k2:2 -m glm-4.5:2 -m mistral:2 --arbiter minimax-m1 --min-iterations 2 --confidence-threshold 95
https://GitHub.com/irthomasthomas/llm-consortium