Live data as of Oct 2026 · 129 subnets · TAO ≈ $299 · Kraken already lists alpha for several roadmap subnets.
Bittensor is a decentralized machine-intelligence network: it pays people (miners) in its own token (TAO) for useful work — compute, data labeling, inference, agents, robotics data — instead of one central company owning the AI. Anyone can join, pick a task, and get paid for doing it well.
Why it's popular (2026):
Every subnet charges a registration lock — TAO you burn to register (mint) a miner slot on that subnet. Think of it as a joining fee that the network itself sets by supply and demand:
Miner dereg risk: track the table's dereg column — when a subnet gets flagged, it means validators are pulling out and the subnet may be deregistered; ROI disappears fast.
Your P&L starts with what you run. The planner's hardware sizing takes GPU capex, GPU power (watts), electricity rate, infra, registration amortization and computes:
What a good machine looks like (drive the planner's tier selector to see the exact math on live data):
| Tier | Typical GPU | Capex | Power | Best for |
|---|---|---|---|---|
| CPU VPS | VPS (CPU-only) | $700 | 80 W | Data / light subnets (lium.io, Kraken-roadmap SNs) |
| 12–16GB | RTX 3060 / 4060 | $600 | 250 W | Entry; CPU-friendly subnets with low VRAM |
| 24GB (recommended) | RTX 3090 / 4090 | $2,500 | 350 W | The balanced start — 24GB VRAM covers most subnets |
| 48GB | A6000 / 2×3090 | $5,000 | 400 W | Multi-model inference / agents |
| 80GB | A100 80GB / H100 | $18,000 | 700 W | Heavy inference subnets (SN3, SN51) |
| 140GB+ | 2×80GB (dual GPU) | $36,000 | 1,400 W | Large-batch mining farms |
Day 1 — open the planner, set electricity + infra, and look at the default sort (best net $/day). That's the current "best subnet to mine" for your hardware.
Picking a subnet — look for: high emission %, decent miners_tao_per_day, low dereg risk, real liquidity, and (for Kraken subnets) an alpha roadmap. The planner's Methodology tab + methodology.html expands every field.
Buy a machine — start with a used 24GB GPU (~$2,000): it's the sweet spot most TMC setups target.
Pitfall for newbies — the metrics are emission-based: if a subnet's emission % looks huge but the alpha price is collapsing, your actual $/day may be near zero. Always cross-check the α price column.
All math is client-side, estimates from live emission share. No model is run on data older than 30 days; the planner recomputes on TAO price changes, and — if all live sources are down — falls back to a bundled snapshot from Sep 2026 so the page never 500s.
your TAO/day = miners_tao_per_day × incentive_shareyour USD/day = your TAO/day × TAO pricenet USD/day = your USD/day − power − infra − amortized registration − amortized capexpayback (days) = GPU capex ÷ net USD/day| Input | Meaning |
|---|---|
| Electricity rate ($/kWh) | Your power cost; most US home rates 0.10–0.20, data centers 0.04–0.08. |
| Infra ($/mo) | Hosting / racks / bandwidth, monthly. |
| Incentive share | % of miners_tao_per_day you assume your UID gets; 0.1 token = 10% of pool. |
| Registration cost (TAO) | The lock cost at your registration moment (doubles per registration, then decays). |
| Amortization (days) | How many days you spread the registration + capex over. |
| GPU capex / power | Hardware purchase + its draw; the sizing tier presets these. |
| TAO price (scenario) | Scenario override for all USD figures. |
| GPU power (scenario) | Override sizing. |
Caveats — read me. Emission is computed from the current emission % snapshot; it is NOT a prediction. α price is volatile: a subnet's miners income is denominated in its own alpha, so a price drop can erase your net $/day even when emission looks great. Hardware capex assumes you buy used GPUs at the tier capex; performance varies. Bittensor subnets carry real risk — subnets get deregistered, α prices crash, and your burn/registration is non-refundable. This tool is for research, not investment advice.
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SubnetROI · [email protected] · Data: TaoMarketCap public API + CoinGecko · This is an estimate tool — always validate against taostats before making decisions.