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luckrig: a concept for tasting LLM rigs, not just models

luckrig: a concept for tasting LLM rigs, not just models

HuggingFace Spaces lets you try models.
LMSys Arena lets you compare models.

Neither lets you try a specific rig.

Exact GPU. Exact quantization. Exact context length.
Someone's actual tuning notes — with your own prompt, right now.

That's the gap. luckrig is a concept to fill it.


If Arena maps models, luckrig maps the rigs.

Service What you taste Hardware visible?
HF Spaces Author's model wrap Whatever they printed
LMSys Arena Blind A/B models Model name. Nothing else.
AI Horde Any worker that fits Abstracted away
luckrig A specific rig GPU · quant · ctx · tuning

AI Horde abstracts the worker away.
luckrig makes the hardware the star.


Access earned by contribution, not money.

Inspired by Hotline Connect — the early-2000s Mac P2P tool where
contribution score, not payment, determined access rights.

Register a node → write tuning notes → upload timing measurements.
That's how you earn access to other people's rigs.


Three seed nodes exist in the POC — not yet public.

  • first-5090-qwen3 — RTX 5090, Qwen3-35B-A3B, Q4_K_XL, 267 tok/s
  • weekend-m3max — Apple M3 Max, Qwen2.5-14B, Q5_K_M
  • shed-pi5 — Raspberry Pi 5, llama3.2-1B, 2.3 tok/s

These are local test nodes to demonstrate the concept.
Looking for early contributors who want to register a real node.


Rarity-first, not leaderboard.

The Pi node ranks higher than the 5090 because it's rarer.
Not a speed competition — a showcase of diversity.


Working POC. No external dependencies.

git clone github.com/prospectorlabs/luckrig
cd luckrig
npm start
http://127.0.0.1:8787

Concept + full spec + working code, all open.

https://github.com/prospectorlabs/luckrig
https://prospectorlabs.dev/luckrig/

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