
Fugal
غ (Ghain)SN70Open competition to train cost-aware LLM routers, with hardware-attested benchmarks.
Overview
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Neuron registration trends

Open competition to train cost-aware LLM routers, with hardware-attested benchmarks.
Neuron registration trends
The alpha price in τ, day by day, and what moved it each way.
Log change over the last 14 days +76.03%: protocol buys 0.00%, root sells −14.30%, users net −111.24%, cross-subnet +201.57%, unattributed 0.00%. 8 days still in progress are counted as far as known.
The chart follows the alpha price in τ. Each period starts at its opening price and steps through the mechanisms below, in a fixed order, to its closing price: a step that rises pushed the price up, one that falls pushed it down, and its height is how far it moved the price. A thin line carries each close into the next period’s open; it is dashed where the two differ. The faint candle behind each period is the market’s own open, high, low and close, for context.
With many periods on screen (90 days, or hours), the steps would be too thin to read, so the chart draws the price as a line instead and colours each period by the mechanism that moved it most. Hover a period for the full step-by-step breakdown.
Not here, because they do not move the spot price: the per-block TAO injection into the pool, and burns.
All figures are log changes, not simple percentage changes, so the parts add up exactly to the total; on big moves the two differ (a 10% log fall is a 9.5% price fall).
2026-11-10 UTC, as of ≈00:25 UTC (block 9,472,216)
The day isn’t over yet, so these figures keep changing. They are in no daily total, average or rank.
Emission history is being recalculated from on-chain allocation.
Organizations deploying large language models face a fundamental tradeoff: routing all requests to the highest-quality model maximizes accuracy but exhausts budgets quickly, while routing to cheap models cuts costs but degrades results. The tradeoff is dynamic—model performance, pricing, and availability change constantly, and organizations lack a continuous, auditable mechanism to measure which model is best for each type of request at current prices.
A decentralized network of independent evaluators, each running their own router and competing on accuracy per dollar, can continuously measure and publish this data. By incentivizing participants to optimize routing decisions and requiring hardware-attested proofs of the work performed, the network becomes both trustworthy and cost-transparent: every routing decision is recorded, graded, and priced in a shared public ledger.
Fugal implements this as a Bittensor subnet where miners train small routing models (linear layers on a frozen backbone), route questions to LLMs inside confidential virtual machines, grade the replies with pinned checkers, and produce Intel TDX-attested proofs of their work. Validators verify the attestations without calling any models themselves, score each miner on accuracy headroom above what any single model or random mixture would achieve at the same cost, and publish the full results—routing decisions, model correctness, costs—so miners can retrain and improve.
Auto-generated from the GitHub repository and may not reflect the latest changes.
A Bittensor subnet for continuously improving cost-aware LLM routing. Miners run benchmarks inside Intel TDX confidential VMs, route questions to language models, pay for inference, grade replies, and produce hardware-attested proofs. Validators verify the attestations and proofs without calling any models, score miners on accuracy headroom above a constant-policy frontier, and set weights.
The subnet outputs a continuously refreshed public record of model performance and costs.
Miners embed questions through a frozen Qwen3-0.6B backbone and route via a trainable linear head (~10K–130KB .npz), selecting models based on question content and price. Routing calls execute inside a metering proxy within an Intel TDX confidential VM, producing hardware-attested BenchmarkProof artifacts that include question IDs, routing decisions, results, costs, and a TDX DCAP quote. Validators collect proofs at a deterministic block, verify DCAP signatures, measurement registers against approved runtime images, content hash bindings, head commitments, answer grading, exploration integrity, and cost consistency.
Validators pool exploration results by coldkey to build a reference frame (upper convex hull of model accuracy vs. cost), score each miner as accuracy headroom above the frontier, dedup near-duplicate heads, apply evidence accumulation and burn-in, compute weights via Yuma consensus, and publish full-epoch reveals (results matrix, routing decisions, scores, frame samples). Miners download reveals, retrain heads, and commit improved versions on-chain for the next epoch.
Score = max(0, wilson_lcb(accuracy) − frontier(cost_per_question)) × burn_in × frontier_confidence, zeroed below 80% of frontier's maximum accuracy. The frontier is the upper convex hull of accuracy vs. cost from reference-frame exploration samples and pinned model prices; it is identical for every miner and independent of field size. Exploration (nonce-chosen model routing, ~5% of questions) is never scored but subsidizes the reference frame.
Evidence accumulates over epochs with EWMA decay (half-life 200 epochs). Burn-in (~3000 questions) ramps new heads from zero. Exploration samples are aggregated by coldkey and median-pooled; duplicated heads are disqualified.
Unassigned weight (1 − frontier_confidence) burns to the subnet owner's UID.
The sections above were auto-generated from the GitHub repository and may not reflect the latest changes. View repository (github.com, opens in a new tab, external site)
Open competition to train cost-aware LLM routers, with hardware-attested benchmarks.
Bittensor subnet for continuous, decentralized LLM router optimization - fugal-ai/fugal-subnet
This information was auto-extracted from the GitHub repository and may be incomplete or out of date. Always refer to the subnet's GitHub repository (github.com, opens in a new tab, external site) for the latest instructions.
Not yet verified: GPU, RAM, Network, Notes
Not yet verified: GPU, RAM, Storage, Notes
Machine-extracted from the subnet's repository docs; may be wrong or out of date. Check the repository before buying or renting hardware. Open the repository (github.com, opens in a new tab, external site) · Extracted 9 Nov 2026
Clone the repository
git clone https://github.com/fugal-ai/fugal-subnet.git && cd fugal-subnet
Create and activate Python virtual environment (Python 3.11-3.12)
python3 -m venv .venv && source .venv/bin/activate
Install Rust (required for bittensor build)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh && source "$HOME/.cargo/env"
Install Fugal subnet and dependencies
pip install -e .
Create wallet coldkey
btcli wallet create --wallet.name fugal_miner
Create wallet hotkey
btcli wallet create --wallet.name fugal_miner --wallet.hotkey default --type hotkey
Register on the subnet (obtain TAO for registration burn)
btcli subnet register --netuid <NETUID> --wallet.name fugal_miner --wallet.hotkey default --network finney
Train a router head on observed data (or use --synthetic for testing)
python scripts/train_head.py --matrix data/matrix.npz --manifest data/benchmark_tokens_v1.json --models openai/gpt-5.4-mini anthropic/claude-haiku-4.5 deepseek/deepseek-v4-flash --output data/my_head.npz --epochs 100
Run the miner in production mode (--live on Intel TDX; --mock for testing)
python neurons/miner.py --netuid <NETUID> --network finney --coldkey fugal_miner --hotkey default --head-path data/my_head.npz --port 8091 --live
Clone the repository
git clone https://github.com/fugal-ai/fugal-subnet.git && cd fugal-subnet
Create and activate Python virtual environment (Python 3.11-3.12)
python3 -m venv .venv && source .venv/bin/activate
Install Rust (required for bittensor build)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh && source "$HOME/.cargo/env"
Install with DCAP verifier (required for --live); alternatively: pip install -e ".[tee]"
uv sync --locked --extra tee
Create wallet coldkey
btcli wallet create --wallet.name fugal_validator
Create wallet hotkey
btcli wallet create --wallet.name fugal_validator --wallet.hotkey default --type hotkey
Register on the subnet with at least FUGAL_MIN_VALIDATOR_STAKE (default 50 TAO equivalent)
btcli subnet register --netuid <NETUID> --wallet.name fugal_validator --wallet.hotkey default --network finney
Set up systemd service and environment file (configure network, netuid, approved entries, epoch interval)(optional)
sudo cp deploy/systemd/fugal-validator.service /etc/systemd/system/ && sudo install -D -m 0600 deploy/env/validator.env.example /etc/fugal/validator.env && sudoedit /etc/fugal/validator.env
Run the validator (--live on mainnet; --mock for testing on test chains)
python neurons/validator.py --netuid <NETUID> --network finney --coldkey fugal_validator --hotkey default --live