--- hip: 0008 title: HMM (Hanzo Market Maker) - Native DEX for AI Compute Resources author: Hanzo AI Team type: Standards Track category: Core status: Draft created: 2025-01-09 requires: HIP-0, HIP-1 --- # HIP-0008: HMM (Hanzo Market Maker) - Native DEX for AI Compute Resources ## Abstract This proposal specifies the HMM (Hanzo Market Maker), a native decentralized exchange built on Hanzo's sovereign L1 blockchain (launching as L2 on Lux) for trading AI compute resources. HMM enables a liquid marketplace for GPU time, model inference, training slots, and other AI resources with dynamic pricing, instant settlement, and cross-chain accessibility. ## Specification ### Core Architecture ```python class HMMExchange: """ Hanzo Market Maker - DEX for AI compute resources """ def __init__(self): self.resource_pools = {} # Liquidity pools for compute types self.order_book = OrderBook() self.pricing_engine = DynamicPricingEngine() self.quality_oracle = QualityMetricsOracle() self.settlement_layer = InstantSettlement() ``` ### Resource Types #### Tradeable Compute Resources ```yaml GPU Compute: - Inference: Real-time model inference (tokens/second) - Training: Batch training slots (GPU-hours) - Fine-tuning: Dedicated fine-tuning resources - Memory: VRAM allocation (GB-hours) Model Access: - HLLM Inference: Access to Hamiltonian models - Custom Models: User-deployed model endpoints - Embeddings: Vector generation services - Agents: Autonomous agent runtime Storage & Data: - Model Storage: Persistent model hosting - Dataset Storage: Training data repositories - Vector DBs: Embedding storage and retrieval - Checkpoints: Training state persistence ``` ### Market Mechanisms #### Automated Market Making (AMM) ```python class ComputeAMM: """ Constant product AMM for compute resources """ def get_price(self, pool, amount_in, resource_type): """ x * y = k pricing formula adapted for compute """ reserve_compute = pool.compute_reserves[resource_type] reserve_hanzo = pool.hanzo_reserves # Apply constant product formula k = reserve_compute * reserve_hanzo new_compute = reserve_compute - amount_in new_hanzo = k / new_compute price = new_hanzo - reserve_hanzo # Apply quality multiplier quality_score = self.oracle.get_quality(resource_type) adjusted_price = price * quality_score return adjusted_price ``` #### Order Book Model ```python class OrderBook: """ Traditional order book for limit orders """ def __init__(self): self.bids = PriorityQueue() # Buy orders self.asks = PriorityQueue() # Sell orders def place_order(self, order_type, resource, amount, price): order = Order( type=order_type, resource=resource, amount=amount, price=price, timestamp=now() ) if order_type == "BID": self.bids.add(order) else: self.asks.add(order) self.match_orders() ``` ### Liquidity Provision #### Resource Pools ```python class ResourcePool: """ Liquidity pool for specific compute resource """ def __init__(self, resource_type): self.resource_type = resource_type self.compute_reserves = 0 # Available compute units self.hanzo_reserves = 0 # HANZO tokens in pool self.lp_tokens = {} # Liquidity provider shares def add_liquidity(self, provider, compute_amount, hanzo_amount): # Calculate LP tokens based on pool share if self.total_lp_tokens == 0: lp_tokens = sqrt(compute_amount * hanzo_amount) else: lp_tokens = min( compute_amount * self.total_lp_tokens / self.compute_reserves, hanzo_amount * self.total_lp_tokens / self.hanzo_reserves ) self.lp_tokens[provider] += lp_tokens self.compute_reserves += compute_amount self.hanzo_reserves += hanzo_amount return lp_tokens ``` ### Quality Metrics & Pricing #### Performance Oracle ```python class QualityMetricsOracle: """ Tracks and reports compute quality metrics """ def __init__(self): self.metrics = { "latency": {}, # Response time "throughput": {}, # Tokens/second "availability": {}, # Uptime percentage "accuracy": {} # Model performance } def update_metrics(self, provider, metrics): # Rolling average of performance metrics for metric, value in metrics.items(): self.metrics[metric][provider] = ( 0.7 * self.metrics[metric].get(provider, value) + 0.3 * value ) def calculate_quality_score(self, provider): # Weighted quality score 0-1 weights = { "latency": 0.3, "throughput": 0.3, "availability": 0.2, "accuracy": 0.2 } score = sum( self.metrics[metric].get(provider, 0.5) * weight for metric, weight in weights.items() ) return score ``` ### Settlement & Execution #### Instant Settlement Layer ```python class InstantSettlement: """ Sub-second settlement for compute trades """ def settle_trade(self, buyer, seller, resource, amount, price): # Atomic swap with atomic_transaction(): # Transfer HANZO from buyer to seller self.transfer_hanzo(buyer, seller, price) # Allocate compute resource allocation = self.allocate_compute( provider=seller, consumer=buyer, resource=resource, amount=amount ) # Create access token access_token = self.create_access_token( allocation=allocation, expires=now() + duration(amount) ) return access_token ``` ### Cross-Chain Bridge ```solidity contract HMMBridge { mapping(address => uint256) public pendingCompute; function bridgeFromEthereum( uint256 amount, bytes32 resourceType ) external payable { // Lock ETH/tokens require(msg.value >= getPrice(amount, resourceType)); // Emit event for Hanzo L2 emit ComputeRequested( msg.sender, amount, resourceType, block.timestamp ); // Hanzo L2 monitors and allocates compute pendingCompute[msg.sender] = amount; } } ``` ## Economic Model ### Fee Structure ```yaml Trading Fees: - Taker: 0.3% of trade value - Maker: 0.1% of trade value - LP Rewards: 0.2% to liquidity providers Quality Incentives: - Performance Bonus: +50% fees for top 10% quality - Penalty: -50% fees for bottom 10% quality - Slashing: Remove from pools for consistent poor performance Volume Discounts: - Tier 1 (>1000 HANZO/month): 10% discount - Tier 2 (>10000 HANZO/month): 20% discount - Tier 3 (>100000 HANZO/month): 30% discount ``` ### Token Utility HANZO token uses in HMM: 1. **Trading**: Buy/sell compute resources 2. **Liquidity**: Provide liquidity to pools 3. **Governance**: Vote on pool parameters 4. **Staking**: Stake for fee discounts 5. **Quality**: Stake as quality collateral ## Security Considerations ### Resource Verification - Cryptographic proof of compute completion - Trusted Execution Environment (TEE) attestation - Slashing for false resource claims ### Price Manipulation Protection - Time-weighted average prices (TWAP) - Maximum price impact limits - Flash loan protection ### Quality Assurance - Continuous performance monitoring - Automated quality scoring - Community reporting system ## References 1. Uniswap v3 Whitepaper (Concentrated Liquidity) 2. Render Network (Distributed GPU Compute) 3. Akash Network (Decentralized Cloud) 4. Ocean Protocol (Data Markets) ## Copyright Copyright and related rights waived via [CC0](https://creativecommons.org/publicdomain/zero/1.0/).