Trustless decentralised machine learning on Sui. GPU miners train models; AWS Nitro Enclave validators score every gradient and sign the proof on-chain; rewards settle in VRAM every 10 minutes — no trusted coordinator, no whitelist.
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This is the open-source SDK for Vram Network — the contracts, on-chain client, adapter framework, operator CLI, and Python sidecar trainer. Everything here is open and auditable.
The miner and validator binaries are closed-source and distributed separately (see below). This repo contains the protocol they speak.
| Component | Path | What it is |
|---|---|---|
| Smart contracts | contracts/sources/ |
13 Sui Move modules — PeerRegistry, ScoreLedger, RewardDistributor, TrainingJobBoard, and more |
| Core types + OpenSkill | vramhub-core/ |
Shared types, Bayesian rating system, error types — no I/O |
| Sui RPC client | vramhub-chain/ |
All on-chain calls: register, submit scores, claim rewards, post training jobs |
| Training adapters | vramhub-adapter/ |
Pluggable adapter trait + impls: Candle nano-GPT, Python sidecar, custom |
| Operator CLI | vramhub-cli/ |
vram-cli register-miner, register-validator, status, scores |
| Python sidecar | python/ |
Run any HuggingFace model as a VRAM miner via HTTP |
| Docs | docs/ |
Architecture, incentives, contract reference, env-var guide |
curl -sSL https://www.vram.network/install.sh | bashSet your wallet mnemonic in ~/.vramhub/.env and run vram-miner. Full guide: ONBOARDING.md — Option B.
| Network | Sui Testnet |
| Reward pool | 6,000,000 VRAM ready to distribute |
| Emission | 70 VRAM / 10-minute window |
| Min stake | 1 SUI (refundable) |
The validator installer lives at VRAM-AI/vram-validator.
curl -sSf https://raw.githubusercontent.com/VRAM-AI/vram-validator/main/install.sh | bashInstalls vram-validator + vram-cli. Full setup guide: ONBOARDING.md — Option C1/C2.
- Test mode — any Linux VPS, simulated scoring, no AWS required
- Nitro enclave — AWS
c5.xlarge, hardware-attested scoring (~$36/mo spot)
Post a training job on vram.network/training. The reference dataset (Sui Move / DeepBook instruction pairs for google/gemma-4-E2B-it) is at VRAM-AI/training.
# Start the Python sidecar on your GPU with the Move dataset
python python/vram_trainer.py \
--model google/gemma-4-E2B-it \
--dataset https://raw.githubusercontent.com/VRAM-AI/training/main/data/train_clean.jsonl \
--device cuda --dtype bfloat16 --batch 2 --seqlen 2048| Object | ID |
|---|---|
| Package | 0xaff18bf6286047126901610d758d8fd111c9215a6e46abc704b6a0be838badd5 |
| PeerRegistry | 0x50a9982f6a3d6c1e6674f0fb4fa8b985007dbe19fc797abc691579be1f6493df |
| ValidatorRegistry | 0x438d0ce63d40210d4e621cca6aaaf5d5438adaa54dfd71383fe41a82692a2561 |
| EnclaveRegistry | 0x442b82e471c1ee4577ea1f2168deb1f0b04fcc861ab79edb4b9c7d7738bf7f9f |
| ScoreLedger | 0x0d2594727abeb45a13763baf8801ae765fbe41d147b28916ca78a0d08f73223a |
| RoundState | 0xc1f18dc92629907641bc3176449af39738d2d8a93b4ad6b22548f4aed91d2611 |
| Hparams | 0x18b884530033f9b3e449b898c540ee5d3a25c4cab0abcf4843ef8e86e12adbfc |
| RewardPool | 0x576ebeb78449ad46ef70dc3c5ca4e38d178846610bd7cf9f0764ae2f1dc0fe93 |
| TrainingJobBoard | 0xb481254350087569f904fe6fc45c337c0905651040791e532e0f044b9fc7474c |
Chain-id 4c78adac · Deployer 0xb7aaeb31…c772c3
# Verify contracts locally — no wallet needed
git clone https://github.com/VRAM-AI/vram-network.git
cd vram-network/contracts && sui move testThe quickest entry points depending on what you want to do:
All 13 Move modules are in contracts/sources/. Run sui move test — 101 tests, all must pass. Good first areas: training_jobs.move (job lifecycle), score_ledger.move (signature verification), reward_distributor.move (halving math).
Implement TrainingFrameworkAdapter in vramhub-adapter/src/. The trait has five methods: train_step, forward_loss, compress_gradient, load_checkpoint, save_checkpoint. See vramhub-adapter/src/sidecar.rs for the simplest reference (~150 lines — it just calls a local HTTP server). See CONTRIBUTING.md for the full walkthrough.
vramhub-chain/src/ has one file per object type. If a new contract method is added, the corresponding Rust function goes here. The client is pure async Rust with no hidden state — easy to extend.
python/vram_trainer.py supports any AutoModelForCausalLM model via --model. To add a new dataset format or training objective, extend _get_batch(). See python/README.md if it exists, otherwise read the docstring at the top of the file.
docs/ is CC BY 4.0. If something is wrong or out of date, open a PR directly — no issue needed for doc fixes.
The fastest way to find real bugs is to actually run a miner or validator and report what breaks. See ONBOARDING.md.
GPU Miner ──── compressed gradient ───→ Walrus (Sui)
│
▼
Validator (AWS Nitro Enclave)
│
attested loss-delta
▼
Sui ScoreLedger
│
Bayesian OpenSkill update
▼
RewardDistributor
│
VRAM per window
▼
Miner wallet
Full threat model and trust assumptions: docs/architecture.md
- Hard cap: 21,000,000 VRAM
- Mining pool (50%): emitted per-window, supply-based halving — Phase 1: 70 VRAM/window → Phase 2: 35 VRAM/window
- TGE pre-mint (50%): Treasury 25% (6m cliff, 48m vest) · Investors 5% (SAFT lock-up) · Team 8% (12m cliff, 36m vest) · Liquidity 7% (instant) · Airdrop 5% (converts from testnet points)
- Validator bonding: 2,100 → 21,000 VRAM burned per slot (4 tiers, max 500 validators)
Full model: docs/incentives.md
| Feature | Status |
|---|---|
| Miner binary (testnet) | ✅ Live |
| Validator — test mode | ✅ Live |
| Validator — Nitro enclave (mainnet) | ⏳ In progress |
| VRAM token payouts | ⏳ Pending first Nitro validator |
| vram-sdk public release | 🗓 Q3 2026 |
| GPU marketplace / rental | 🗓 Planned |
- Contracts (
contracts/) — MIT - SDK crates (
vramhub-*/) — Apache 2.0 - Validator installer (VRAM-AI/vram-validator) — MIT
- Miner / Validator binaries — proprietary; freely installable under LICENSE-BINARIES.md
Security issues: email security@vram.ai — see SECURITY.md. Do not open public issues for vulnerabilities.
Team: team@vram.ai · Builders: builders@vram.ai · © 2024-2026 VRAM AI Limited