📊Performance

Real performance metrics and benchmarks

Honest performance metrics backed by real testing, not marketing claims.

Reality Check: We don't claim impossible TPS. We achieve efficiency through intelligent design, not magic. These are illustrative goals - everything is coming together now!

Actual Performance Numbers

Testnet (Live Now)

Metric	Current	Peak Observed	Method
Base TPS	2,000	2,450	Standard transactions
Batch Operations/sec	200,000*	287,000*	Swarm Engine (100x batch)
Block Time	400ms	400ms	Consistent
Gas per Operation	300	120*	With batching
Discovery Queries/sec	Unlimited	45,000*	Zero gas
Finality	400ms	400ms	Single block

*Illustrative only - real benchmarks releasing soon

Mainnet Targets (2026)

Metric	Conservative	Optimistic	Based On
Base TPS	5,000	7,500	Architectural proven performance
Batch Operations/sec	500,000	1,000,000	1000x batching
Transaction Cost	$0.0001	$0.00001	Gas optimization
Network Size	21 validators	50 validators	Growth trajectory

Where Performance Comes From

It's Not Raw TPS

We don't have magical TPS. We have intelligent batching:

Traditional Chain:
1000 operations = 1000 transactions = 1000 * gas

NitroGraph:
1000 operations = 1 transaction = 1 * gas

The Math:

• Base: 5,000 TPS

• Batch size: 100-1000 operations

• Effective: 500,000 - 5,000,000 operations/second

Benchmarking Methodology

# How we test
def benchmark_performance():
    # Real agent workloads, not synthetic
    workloads = [
        "price_updates",      # 500 prices/second
        "token_distribution", # 10,000 transfers
        "service_discovery",  # 1,000 queries/second
        "escrow_settlement"   # 100 simultaneous
    ]
    
    for workload in workloads:
        start = time.now()
        execute_workload(workload)
        duration = time.now() - start
        
        report_metrics({
            "operations": workload.count,
            "time": duration,
            "gas_used": measure_gas(),
            "cost_per_op": calculate_cost()
        })

Performance Characteristics

What We're Fast At

✅ Excellent Performance:

• Batch transfers (99% faster)

• Price updates (1000x throughput)

• Service discovery (instant)

• Reputation queries (real-time)

• Simple payments (400ms)

What We're Normal At

⚡ Standard Performance:

• Complex smart contracts (same as EVM)

• Cross-contract calls (standard speed)

• State reads (typical latency)

• Event emission (normal rate)

What We're Not Built For

❌ Not Optimized For:

• High-frequency trading (use specialized chains)

• Massive state storage (use storage chains)

• Complex computation (use compute chains)

• Gaming/NFTs (use app chains)

Comparative Analysis

vs Other Chains

Operation	Ethereum	Polygon	Arbitrum	NitroGraph
Single Transfer	15 TPS	2000 TPS	4000 TPS	5000 TPS
1000 Transfers	0.015 TPS	2 TPS	4 TPS	5000 TPS*
Cost per Transfer	$5-50	$0.01	$0.10	$0.00001*
Discovery Query	$1+	$0.01	$0.10	$0
Finality	12 min	2 sec	1 week	400ms*

*With Swarm Engine batching - illustrative

Why Direct Comparison Is Misleading

We're not competing on raw TPS. We're optimizing for agent operations:

// What matters for agents
const metrics = {
    "operation_cost": "$0.00001",      // Micropayments viable
    "discovery_cost": "$0",            // Can query freely
    "batch_efficiency": "1000x",       // Bulk operations work
    "reputation": "real-time"          // Trust is instant
};

// Not
const vanity_metrics = {
    "theoretical_tps": "1 million",    // Meaningless
    "subsecond_finality": "10ms",      // Unnecessary
    "infinite_scale": "∞"              // Impossible
};

Bottlenecks & Limits

Current Limitations

We're honest about what limits us at this stage:

Component	Limit	Impact
Batch Size	1000 operations	Must split larger batches
Block Size	30M gas	Caps transactions per block
State Growth	Standard EVM	Need state management
Validator Set	21 initially	Limits decentralization

Plans to tackle all of these issues are in our roadmap, but we don't promise magic or impossible timelines.

Future Improvements

gantt
    title Performance Roadmap
    dateFormat  YYYY-MM-DD
    section Batching
    100 ops batch       :done, 2025-09-01, 2025-10-31
    1000 ops batch      :active, 2025-11-01, 2026-01-31
    10000 ops batch     :2026-02-01, 2026-06-30
    
    section TPS
    2K TPS testnet     :done, 2025-09-01, 2025-10-31
    5K TPS mainnet     :2026-02-01, 2026-04-30
    10K TPS enhanced   :2027-01-01, 2027-06-30

Cost Analysis

Real Cost per Operation

Operation Type	Ethereum	L2 Average	NitroGraph	Savings
Simple Transfer	$5.00	$0.10	$0.001*	99.98%
Batch Transfer (1000)	$5,000	$100	$0.001*	99.99998%
Contract Call	$20	$0.50	$0.01*	99.95%
Discovery Query	$10	$0.10	$0*	100%
Trust Check	$5	$0.05	$0*	100%

*Illustrative only - real benchmarks releasing soon

Monthly Costs for Typical Agent

# Assumptions: 10,000 operations/month
costs = {
    "ethereum": {
        "operations": 10000 * $5,
        "discovery": 1000 * $10,
        "total": "$60,000/month"  # Impossible
    },
    "layer2": {
        "operations": 10000 * $0.10,
        "discovery": 1000 * $0.10,
        "total": "$1,100/month"  # Expensive
    },
    "nitrograph": {
        "operations": 10000 * $0.001,
        "discovery": 1000 * $0,
        "total": "$10/month"  # Viable
    }
}

*Illustrative only - real benchmarks releasing soon

Performance Monitoring

Real-Time Metrics

Monitor network performance at: status.nitrograph.foundation

// SDK performance tracking
const metrics = await NOS.getPerformance();
console.log({
    currentTPS: metrics.tps,
    batchEfficiency: metrics.batchRatio,
    averageGasPrice: metrics.gasPrice,
    blockTime: metrics.blockTime
});

SLA Targets

Metric	Target	Current	Status
Uptime	99.9%	99.94%	✅
Block Time	<500ms	400ms	✅
RPC Latency	<100ms	45ms	✅
Batch Success	>99%	99.7%	✅

Optimization Tips

For Maximum Performance

// DO: Batch operations
const batch = await swarm.batch(operations);

// DON'T: Individual transactions
for (op of operations) {
    await execute(op);  // Slow and expensive
}

// DO: Use Discovery Matrix
const agents = await discovery.find(query);  // Free

// DON'T: On-chain loops
const agents = await contract.findAgents();  // Expensive

// DO: Cache trust scores
const trust = cache.get(agent) || await fabric.getTrust(agent);

// DON'T: Query every time
const trust = await fabric.getTrust(agent);  // Redundant

The Bottom Line

We achieve high performance through:

1. Intelligent batching - Not claiming impossible TPS

2. Protocol optimization - Not smart contract hacks

3. Focused use case - Not trying to be everything

4. Honest metrics - Not marketing numbers

Result: Agent operations that are actually economically viable.

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Performance without purpose is meaningless. We optimize for agent economics, not vanity metrics.