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.

Protocol buy: The chain spending excess emission TAO to buy alpha from the pool. A permanent protocol buyback; it pushes the price up.
Root sell: Root-dividend claims sold into the pool; they push the price down.
User buy / sell: Staking into and unstaking from this subnet.
Cross-subnet: Swaps from other subnets routed through this pool.
Residual: What the decomposition cannot attribute to a mechanism. It is always shown, never folded into another part.

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).

Today so far

2026-11-12 UTC, as of ≈13:47 UTC (block 9,490,625)

Price, today so far: 0.01418 τ
Volume, today so far: 125.05 τ
Market cap incl. burned, today so far: 43.02K τ
TAO in pool, today so far: 48.80K τ
Emission share, today so far: —not available yet

The day isn’t over yet, so these figures keep changing. They are in no daily total, average or rank.

Subnet Configuration

Pool & Mechanism

AMM Pool

Balancer

Mechanism

dTAO

Access

Registration

Open

Neurons

256/256Competitive

Validators

13/6451 open

Economics

Owner Cut

18.00%

Weights

Disabled

Disabled

1–65535

Timing

Tempo

360~1.2h

Immunity Period

5,000~16.7h

Activity Cutoff

5,000~16.7h

Market & Economics

Market

Market Cap

27.77K τ

FDV

91.82K τ

TVL

97.60K τ

Lifetime Volume

2.88M τ

Holders

4,062−1.22% 7d·+2.45% 30d

Circulating alpha

1,958,584.6371 ج

Owner Locked

264,724.1855 ج(13.52% of circulating)Perpetual

14d price

Holder & circulating-alpha metrics as of 2026-11-12 00:00 UTC (daily close)

Revenue / Recycle

> 1,000×

last day · Net positive

Emissions

1.62%of network emissions

TAO emitted to this subnet

Sent into its pool each day: its share of network emissions

58.1997 τ/day

58.1997 τ per day

Daily distribution

Alpha paid out each day, valued at the pool price

7,200.0000 ج/day

≈ 102.0797 τ·102.0797 τ per day

Miners43.91%

3,161.4815 ج ≈ 44.8227 τ/day

Validators38.09%

2,742.5515 ج ≈ 38.8832 τ/day

Owner18.00%

1,295.9670 ج ≈ 18.3739 τ/day

Stake 1,000 τ here

56.1998 ج/day≈ 0.7968 τ·0.7968 τ per day

From the validator share, before commission

53.59%staker APY

· 30d avg 53.59% · Stable

90d

Pool emits 1.0000 ج/block at 0.01418 τ/ج. Yield varies with stake weight, pool price, and emission rate.

For Miners

43.91% of emission

Slots

256/256Competitive

Burn Cost

0.0005 τ

Est. per Miner

26.3457 ج/epoch≈ 0.3735 τ

Pool / Epoch

158.0741 ج≈ 2.2411 τ

For Validators & Stakers

38.09% of emission

Validator Slots

13/6451 open

Est. per Validator

10.5483 ج/epoch≈ 0.1496 τ

Registration Activity

Neuron registration trends

This Interval

24h: 0 registrations (0 burn · 0 PoW)

Target / Interval

Slots Available

Competitive

256/256

Neurons

256

Development Activity

gradients-ai/G.O.DPython

38417 contributors25 commits/30dLast commit 0d ago

Apache-2.0

Top Contributors

What Gradients does

Organizations training large language models and diffusion models face high compute costs, limited access to specialized GPU infrastructure, and difficulty evaluating competing training approaches at scale. Running tournaments requires coordinating multiple trainers, validating code quality, managing GPU allocation, and fairly comparing results — logistical overhead that slows iteration.

A decentralized network model distributes this coordination: miners submit open-source training code, a validator network orchestrates the execution on shared GPU infrastructure, runs standardized evaluation, and ranks winners transparently. Costs are socialized across participants, redundant work is eliminated through tournament structure, and code quality is enforced upfront through obfuscation detection and source code validation.

G.O.D (Gradients on Demand) implements this on Bittensor by running weekly text and image training tournaments. Miners submit GitHub repositories and commit SHAs; validators clone, build Docker images, run training on their GPU clusters, upload results to Hugging Face, and score models on held-out evaluation sets.

Tournament fees are deducted per entry (0.7 TAO for text, 0.4 TAO for image), winners are advanced through knockout rounds, and final rankings determine emissions allocation. Winning code is published to a public GitHub organization.

Auto-generated from the GitHub repository and may not reflect the latest changes.

About

active

G.O.D (Gradients on Demand) is a Bittensor subnet that runs recurring tournament competitions where miners submit open-source training code for text and image tasks. Validators orchestrate the competitions by requesting repositories from miners, validating and running the code on GPU infrastructure, evaluating the resulting models, and advancing winners through tournament rounds while distributing emissions based on performance.

How it works

The G.O.D subnet consists of three main services: Miners expose a GET /training_repo/{task_type} endpoint that returns their GitHub repository and commit SHA; Validators orchestrate weekly tournaments by querying miners, validating submissions, distributing tasks to Trainers, and scoring results; Trainers clone miner repositories, build Docker images, run training on GPUs, and upload resulting models to Hugging Face. Validators depend on Postgres (state), Redis (caching), S3 (artifact storage), and Fiber (subnet messaging). Miner code is validated for obfuscation, required files (LICENSE, NOTICE, Dockerfiles), and source readability before tournament entry.

Training containers receive standardized CLI arguments and environment variables, mount read-only cache volumes with models/datasets, and write outputs to /app/checkpoints/{task_id}/{repo_name} for upload. Tournament progression follows group → knockout → boss-round structure with per-sample decision logic for knockout/boss rounds.

Incentive mechanism

Text tournaments use InstructTextTask, DpoTask, GrpoTask, and ChatTask types progressing through rounds (group → knockout → boss). Image tournaments use diffusion/image tasks. Round 1 is always a group stage; later rounds use knockout for ≤8 miners or groups for >8.

Knockout matchups are decided per-sample on held-out test sets with tie deadzones (0.01 nats). Boss round requires winning ≥55% of decided samples AND meeting mean gap requirements. Winner must win ≥5/6 boss tasks (and all continuous-SFT tasks for text) to dethrone the defending champion.

Scoring uses exponential decay (76/19/5 split for three paid ranks) with optional 2x boost for champions exceeding 0.10 performance threshold. Active participants receive 0.0001 weight. Tournament fees (0.7 TAO text, 0.4 TAO image) are deducted after validation.

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)

On-Chain Identitysigned on-chain by subnet owner

Gradients

Jeem · Arabic letter · pronounced “jeem”

Best AutoML plaftorm in the world

Owner: 5EJ1z…VL5g
Hotkey: 5GU4X…VXFu
Owner Cut: 18.00%
Contact: [email protected]

Registered: 1y 11mo ago(Nov 21, 2024)
Block: 4,312,927
Slot history: Every subnet in SN56
Neurons: 256/256
Tempo: 360 blocks

Website

URL: https://www.gradients.io/ (gradients.io, opens in a new tab, external site)
Status: Up200
TLS: Valid
Title: Gradients | Anyone Can Train AI
Checked: 2026-11-12 04:02 UTC

Changes (10)

2026-11-12 04:02 UTCUp200Gradients | Anyone Can Train AI
2026-11-11 04:02 UTCUp200Gradients | Anyone Can Train AI
2026-11-10 04:02 UTCUp200Gradients | Anyone Can Train AI
2026-11-09 04:02 UTCUp200Gradients | Anyone Can Train AI
2026-11-08 04:03 UTCUp200Gradients | Anyone Can Train AI
2026-11-07 04:02 UTCUp200Gradients | Anyone Can Train AI
2026-11-06 04:02 UTCUp200Gradients | Anyone Can Train AI
2026-11-05 04:02 UTCUp200Gradients | Anyone Can Train AI
2026-11-04 04:01 UTCUp200Gradients | Anyone Can Train AI
2026-11-03 04:03 UTCUp200Gradients | Anyone Can Train AI

Subnet History

SubnetIdentitySet 2026-03-22 19:42 UTC

Block 7,803,309Extrinsic

SubnetIdentitySet 2025-03-07 15:34 UTC

Block 5,076,904Extrinsic

Subnet registered 2024-11-21 08:47 UTC

Block 4,312,927Extrinsic

Recent Activity

SubnetIdentitySet Block 7,803,309 Extrinsic2026-03-22 19:42 UTC
SubnetIdentitySet Block 5,076,904 Extrinsic2025-03-07 15:34 UTC
NetworkAdded Block 4,312,927 Extrinsic2024-11-21 08:47 UTC

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.

Hardware Requirements

Miner

GPU—

Networkport 7999

Miners do not need to provide tournament compute; only a running miner endpoint so validators can request repository information

Validator

RAM110 GB

CPU Cores24

Not yet verified: GPU, OS, Network

Trainer nodes need Docker with GPU support, Hugging Face credentials, and sufficient disk for model/dataset/cache volumes

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

Miner Setup

Clone the G.O.D repository

git clone https://github.com/rayonlabs/G.O.D.git && cd G.O.D

Install system dependencies

task bootstrap

Install Python package dependencies

task install

Generate miner configuration (.1.env) with wallet, hotkey, network, and stake threshold settings

task miner-config

btcli s register --netuid 56

Post miner IP to metagraph at least 1 hour before tournament entry

fiber-post-ip --netuid 56 --subtensor.network finney --external_port 7999 --wallet.name default --wallet.hotkey default --external_ip YOUR_PUBLIC_IP

Start the miner service on port 7999

task miner

Configure the GET /training_repo/{task_type} endpoint to return your GitHub repository, commit SHA, optional token, and requested datasets

Edit miner/endpoints/training_repo.py to return your repository details

Fund tournament participation (0.7 TAO for text, 0.4 TAO for image tournaments)

Transfer TAO to collection address 5Ef5JgNv14LY4UEQFHbRQkf8TnegDV3AfAbcsJe5T2w6VQdo

Validator Setup

Clone the G.O.D repository

git clone https://github.com/rayonlabs/G.O.D.git && cd G.O.D

Install system dependencies on Ubuntu

task bootstrap

Install Python package with validator dependencies

pip install -e '.[dev,organic,chain,evaluation-remote,validator]'

Generate validator configuration (.vali.env) with wallet, hotkey, subtensor, database, S3, and port settings

task config

Add model hash salt to validator configuration

echo "MODEL_HASH_SALT=$(openssl rand -hex 32)" >> .vali.env

Initialize Postgres database migrations

task dbup

Start the validator with compose stack (Postgres, Redis, and validator service)

task validator

Optional: Deploy Grafana/Loki/Prometheus for logging and monitoring(optional)

task deploy-observability-server

No hardware? Back this subnet by staking instead New to mining? Start with the setup guide Validator setup guide

Day (UTC)

2026-11-11

1.62%

2026-11-10

1.64%

2026-11-09

1.66%

2026-11-08

1.68%

2026-11-07

1.69%

2026-11-06

1.72%

2026-11-05

1.72%

2026-11-04

1.73%

2026-11-03

1.74%

2026-11-02

1.71%

2026-11-01

1.70%

2026-10-31

1.68%

2026-10-30

1.60%

2026-10-29

1.63%

2026-10-28

1.64%

2026-10-27

1.65%

2026-10-26

1.68%

2026-10-25

1.70%

2026-10-24

1.70%

2026-10-23

1.71%

2026-10-22

1.68%

2026-10-21

1.64%

2026-10-20

1.64%

2026-10-19

1.62%

2026-10-18

1.61%

2026-10-17

1.63%

2026-10-16

1.66%

2026-10-15

1.68%

2026-10-14

1.69%

2026-10-13

1.68%

2026-10-12

1.68%

2026-10-11

1.68%

2026-10-10

1.69%

2026-10-09

1.70%

2026-10-08

1.70%

2026-10-07

1.72%

2026-10-06

1.74%

2026-10-05

1.73%

2026-10-04

1.71%

2026-10-03

1.77%

2026-10-02

1.81%

2026-10-01

1.76%

2026-09-30

1.89%

2026-09-29

1.83%

2026-09-28

1.82%

2026-09-27

1.86%

2026-09-26

2.00%

2026-09-25

1.97%

2026-09-24

1.95%

2026-09-23

1.93%

2026-09-22

1.95%

2026-09-21

1.93%

2026-09-20

1.94%

2026-09-19

1.88%

2026-09-18

1.83%

2026-09-17

1.85%

2026-09-16

1.89%

2026-09-15

1.92%

2026-09-14

1.98%

2026-09-13

1.96%

2026-09-12

1.97%

2026-09-11

1.95%

2026-09-10

1.91%

2026-09-09

2.05%

2026-09-08

2.31%

2026-09-07

2.37%

2026-09-06

2.43%

2026-09-05

0.86%

2026-09-04

0.08%

2026-09-03

0.08%

2026-09-02

0.08%

2026-09-01

0.09%

2026-08-31

0.09%

2026-08-30

0.10%

2026-08-29

0.11%

2026-08-28

0.11%

2026-08-27

0.12%

2026-08-26

0.08%

2026-08-25

0.09%

2026-08-24

0.09%

2026-08-23

0.09%

2026-08-22

0.11%

2026-08-21

0.13%

2026-08-20

0.12%

2026-08-19

0.13%

2026-08-18

0.17%

2026-08-17

0.21%

2026-08-16

0.21%

2026-08-15

0.23%

2026-08-14

0.24%

What moved the price

Overview

Today so far

Subnet Configuration

Market & Economics

For Miners

For Validators & Stakers

Registration Activity

Development Activity

What Gradients does

About

Website

Subnet History

Recent Activity

Participate

Hardware Requirements

Miner Setup

Validator Setup

What moved the price

Today so far

Subnet Configuration

Market & Economics

For Miners

For Validators & Stakers

What Gradients does

About

Website

Subnet History

Hardware Requirements

Miner Setup

Validator Setup