synor/docs/PLAN/PHASE11-Synor-Compute-L2-Part2-HyperEfficiency.md

Phase 11 Part 2: Hyper-Efficient Distributed Compute

Goal: 90% cost reduction vs AWS/GCP/Azure + 10x speed improvement through innovative architecture

---

Executive Summary

Traditional cloud providers have structural inefficiencies:

• 30-60% profit margins built into pricing
• Centralized data centers with high real estate/cooling costs
• Idle capacity that customers pay for but don't use
• Geographic lock-in preventing arbitrage on electricity costs
• Billions of idle consumer devices completely untapped

Synor Compute eliminates these inefficiencies through:

1. Protocol-only overhead (no corporate margin)
2. Distributed infrastructure (homes, offices, edge locations)
3. Real-time spot markets (fill idle capacity instantly)
4. Global electricity arbitrage (route to cheapest regions)
5. Consumer device mesh (phones, browsers, desktops)

---

Part 1: Cost Reduction Architecture

1.1 Zero-Margin Protocol Design

// synor-compute/src/economics/pricing.rs

/// Dynamic pricing engine with near-zero overhead
pub struct DynamicPricingEngine {
    /// Base cost = provider's actual cost (electricity + depreciation)
    base_cost_calculator: BaseCostCalculator,
    /// Protocol fee (only fee in system)
    protocol_fee_percent: f32,  // 5-10% for network sustainability
    /// Real-time supply/demand
    market_state: MarketState,
    /// Geographic cost map
    geo_costs: GeoCostMap,
}

impl DynamicPricingEngine {
    /// Calculate price for compute job
    pub fn calculate_price(&self, job: &ComputeJob) -> Price {
        // 1. Calculate provider's actual cost
        let base_cost = self.base_cost_calculator.compute_cost(
            job.resources(),
            job.duration_estimate(),
            job.provider_location(),
        );

        // 2. Apply supply/demand multiplier (0.5x to 2x)
        let demand_multiplier = self.market_state.demand_multiplier(
            job.resource_type(),
            job.urgency(),
        );

        // 3. Add minimal protocol fee
        let protocol_fee = base_cost * self.protocol_fee_percent;

        Price {
            base: base_cost,
            demand_adjustment: base_cost * (demand_multiplier - 1.0),
            protocol_fee,
            total: base_cost * demand_multiplier + protocol_fee,
        }
    }
}

/// Provider's actual operating cost
pub struct BaseCostCalculator {
    /// Electricity cost by region ($/kWh)
    electricity_rates: HashMap<Region, f64>,
    /// Hardware depreciation rates
    depreciation: HardwareDepreciation,
    /// Cooling efficiency (PUE)
    pue_by_climate: HashMap<Climate, f64>,
}

impl BaseCostCalculator {
    pub fn compute_cost(
        &self,
        resources: &Resources,
        duration: Duration,
        location: &GeoLocation,
    ) -> f64 {
        let region = location.region();
        let electricity_rate = self.electricity_rates.get(&region).unwrap_or(&0.10);
        let pue = self.pue_by_climate.get(&location.climate()).unwrap_or(&1.5);

        // Power consumption
        let power_kw = resources.estimated_power_kw();
        let energy_kwh = power_kw * duration.as_hours() * pue;
        let electricity_cost = energy_kwh * electricity_rate;

        // Hardware depreciation
        let depreciation_cost = self.depreciation.cost_per_hour(resources)
            * duration.as_hours();

        // Network cost (minimal for most jobs)
        let network_cost = resources.network_gb() * 0.01; // $0.01/GB

        electricity_cost + depreciation_cost + network_cost
    }
}

1.2 Geographic Electricity Arbitrage

// synor-compute/src/scheduler/geo_arbitrage.rs

/// Routes compute to cheapest electricity regions
pub struct GeoArbitrageScheduler {
    /// Real-time electricity prices by region
    electricity_prices: Arc<RwLock<HashMap<Region, ElectricityPrice>>>,
    /// Provider locations and capabilities
    providers: ProviderRegistry,
    /// Latency requirements
    latency_constraints: LatencyConstraints,
}

/// Real-time electricity pricing
pub struct ElectricityPrice {
    pub region: Region,
    pub price_per_kwh: f64,
    pub carbon_intensity: f64,  // gCO2/kWh
    pub renewable_percent: f64,
    pub timestamp: Timestamp,
    pub forecast_24h: Vec<f64>,  // Predicted prices
}

impl GeoArbitrageScheduler {
    /// Find cheapest region for job
    pub async fn find_optimal_region(
        &self,
        job: &ComputeJob,
    ) -> Result<SchedulingDecision, Error> {
        let prices = self.electricity_prices.read();

        // Get regions with available capacity
        let available_regions = self.providers
            .regions_with_capacity(job.resources())
            .await?;

        // Filter by latency requirements
        let viable_regions: Vec<_> = available_regions
            .into_iter()
            .filter(|r| self.meets_latency_requirements(r, job))
            .collect();

        // Sort by total cost (electricity + network)
        let mut scored: Vec<_> = viable_regions
            .iter()
            .map(|region| {
                let electricity = prices.get(region).map(|p| p.price_per_kwh).unwrap_or(0.15);
                let network_cost = self.network_cost_to_user(region, job.user_location());
                let total_score = electricity * job.estimated_kwh() + network_cost;
                (region, total_score)
            })
            .collect();

        scored.sort_by(|a, b| a.1.partial_cmp(&b.1).unwrap());

        Ok(SchedulingDecision {
            region: scored[0].0.clone(),
            estimated_cost: scored[0].1,
            alternatives: scored[1..].iter().map(|(r, c)| (*r, *c)).collect(),
        })
    }
}

/// Electricity price feeds from multiple sources
pub struct ElectricityPriceFeed {
    sources: Vec<Box<dyn ElectricityDataSource>>,
}

#[async_trait]
pub trait ElectricityDataSource: Send + Sync {
    async fn get_prices(&self, regions: &[Region]) -> Result<Vec<ElectricityPrice>, Error>;
}

// Implementations for various markets:
// - US: PJM, CAISO, ERCOT, MISO, etc.
// - Europe: EPEX SPOT, Nord Pool
// - Asia: JEPX (Japan), KPX (Korea)

1.3 Spot Market for Idle Capacity

// synor-compute/src/market/spot.rs

/// Real-time spot market for compute resources
pub struct SpotMarket {
    /// Order book for each resource type
    order_books: HashMap<ResourceType, OrderBook>,
    /// Matching engine
    matcher: MatchingEngine,
    /// Price discovery
    price_discovery: PriceDiscovery,
}

/// Compute resource order
pub struct SpotOrder {
    pub order_id: OrderId,
    pub order_type: OrderType,
    pub resource_type: ResourceType,
    pub quantity: ResourceQuantity,
    pub price_limit: Option<f64>,  // None = market order
    pub duration: Duration,
    pub preemptible: bool,  // Can be interrupted
    pub constraints: JobConstraints,
}

pub enum OrderType {
    /// Provider offering compute
    Ask {
        provider_id: ProviderId,
        available_until: Timestamp,
        interruptible: bool,
    },
    /// User requesting compute
    Bid {
        user_id: UserId,
        deadline: Option<Timestamp>,
        priority: Priority,
    },
}

impl SpotMarket {
    /// Submit order to market
    pub async fn submit_order(&self, order: SpotOrder) -> Result<OrderResult, Error> {
        let book = self.order_books.get_mut(&order.resource_type)?;

        match order.order_type {
            OrderType::Ask { .. } => {
                // Provider offering capacity
                book.add_ask(order.clone());

                // Try to match with existing bids
                let matches = self.matcher.match_asks(&book);
                self.execute_matches(matches).await
            }
            OrderType::Bid { .. } => {
                // User requesting capacity
                if let Some(price_limit) = order.price_limit {
                    // Limit order - add to book
                    book.add_bid(order.clone());
                }

                // Try to match immediately
                let matches = self.matcher.match_bids(&book, &order);
                self.execute_matches(matches).await
            }
        }
    }

    /// Get current spot price for resource
    pub fn spot_price(&self, resource: &ResourceType) -> SpotPrice {
        let book = &self.order_books[resource];
        SpotPrice {
            bid: book.best_bid(),
            ask: book.best_ask(),
            last_trade: book.last_trade_price(),
            volume_24h: book.volume_24h(),
        }
    }
}

/// Preemptible compute (like AWS Spot Instances)
pub struct PreemptibleCompute {
    /// Discount vs on-demand (typically 70-90%)
    discount_percent: f32,
    /// Warning time before preemption
    warning_seconds: u32,
    /// Checkpoint strategy
    checkpoint: CheckpointStrategy,
}

impl PreemptibleCompute {
    /// Price at 10-30% of on-demand
    pub fn calculate_price(&self, on_demand_price: f64) -> f64 {
        on_demand_price * (1.0 - self.discount_percent as f64 / 100.0)
    }

    /// Handle preemption gracefully
    pub async fn preempt(&self, job: &mut ComputeJob) -> Result<(), Error> {
        // 1. Send warning to job
        job.send_preemption_warning(self.warning_seconds).await?;

        // 2. Trigger checkpoint if configured
        if let CheckpointStrategy::Auto = self.checkpoint {
            job.checkpoint().await?;
        }

        // 3. Migrate or terminate
        if let Some(new_capacity) = self.find_alternative_capacity(job).await? {
            job.migrate_to(new_capacity).await
        } else {
            job.terminate_gracefully().await
        }
    }
}

1.4 Cost Comparison Calculator

// synor-compute/src/economics/comparison.rs

/// Compare Synor vs traditional cloud pricing
pub struct CostComparison {
    synor_pricing: DynamicPricingEngine,
    aws_pricing: AwsPricingData,
    gcp_pricing: GcpPricingData,
    azure_pricing: AzurePricingData,
}

impl CostComparison {
    pub fn compare(&self, workload: &Workload) -> ComparisonResult {
        let synor_cost = self.synor_pricing.calculate_total(workload);
        let aws_cost = self.aws_pricing.calculate_total(workload);
        let gcp_cost = self.gcp_pricing.calculate_total(workload);
        let azure_cost = self.azure_pricing.calculate_total(workload);

        let min_cloud = aws_cost.min(gcp_cost).min(azure_cost);
        let savings_percent = ((min_cloud - synor_cost) / min_cloud) * 100.0;

        ComparisonResult {
            synor: synor_cost,
            aws: aws_cost,
            gcp: gcp_cost,
            azure: azure_cost,
            savings_vs_cheapest: savings_percent,
            savings_breakdown: self.breakdown_savings(workload),
        }
    }

    fn breakdown_savings(&self, workload: &Workload) -> SavingsBreakdown {
        SavingsBreakdown {
            // No cloud margin
            margin_elimination: 35.0,  // ~35% of cloud pricing is margin
            // Distributed infrastructure
            infrastructure_savings: 15.0,  // No data center overhead
            // Spot/preemptible usage
            spot_savings: 20.0,  // If using preemptible
            // Geographic arbitrage
            geo_arbitrage: 10.0,  // Routing to cheap electricity
            // Consumer device usage (if applicable)
            consumer_devices: 10.0,  // Free compute from devices
            // Total
            total: 90.0,
        }
    }
}

---

Part 2: 10x Speed Architecture

2.1 Intelligent Caching Layer

// synor-compute/src/acceleration/cache.rs

/// Multi-tier caching for inference acceleration
pub struct InferenceCache {
    /// L1: Hot cache in GPU memory
    gpu_cache: GpuCache,
    /// L2: Warm cache in system memory
    memory_cache: MemoryCache,
    /// L3: Cold cache on NVMe
    nvme_cache: NvmeCache,
    /// L4: Distributed cache across nodes
    distributed_cache: DistributedCache,
    /// Semantic cache for similar queries
    semantic_cache: SemanticCache,
}

impl InferenceCache {
    /// Check all cache tiers for result
    pub async fn get(&self, request: &InferenceRequest) -> Option<CachedResult> {
        // 1. Exact match in hot cache (sub-ms)
        if let Some(result) = self.gpu_cache.get(&request.hash()).await {
            return Some(CachedResult::exact(result));
        }

        // 2. Exact match in memory cache (~1ms)
        if let Some(result) = self.memory_cache.get(&request.hash()).await {
            // Promote to GPU cache
            self.gpu_cache.insert(&request.hash(), &result).await;
            return Some(CachedResult::exact(result));
        }

        // 3. Semantic similarity search (~5ms)
        if let Some((similar_req, result, similarity)) =
            self.semantic_cache.find_similar(request, 0.95).await
        {
            // If >95% similar, reuse result
            return Some(CachedResult::semantic(result, similarity));
        }

        // 4. Check distributed cache (~10-50ms)
        if let Some(result) = self.distributed_cache.get(&request.hash()).await {
            self.memory_cache.insert(&request.hash(), &result).await;
            return Some(CachedResult::exact(result));
        }

        None
    }
}

/// Semantic cache using embeddings
pub struct SemanticCache {
    /// Embedding model for queries
    embedder: EmbeddingModel,
    /// Vector index for similarity search
    index: VectorIndex,
    /// Cached results
    results: HashMap<QueryHash, InferenceResult>,
}

impl SemanticCache {
    /// Find semantically similar cached query
    pub async fn find_similar(
        &self,
        request: &InferenceRequest,
        min_similarity: f32,
    ) -> Option<(InferenceRequest, InferenceResult, f32)> {
        // Embed the query
        let embedding = self.embedder.embed(&request.input).await?;

        // Search for similar
        let results = self.index.search(&embedding, 1, min_similarity).await;

        results.first().map(|r| {
            let cached_result = self.results.get(&r.id).unwrap();
            (r.request.clone(), cached_result.clone(), r.similarity)
        })
    }
}

2.2 Speculative Execution

// synor-compute/src/acceleration/speculative.rs

/// Speculative execution for predictable workloads
pub struct SpeculativeExecutor {
    /// Prediction model for next likely requests
    predictor: RequestPredictor,
    /// Pre-computed results
    precomputed: PrecomputedResults,
    /// Background speculation workers
    workers: Vec<SpeculationWorker>,
}

impl SpeculativeExecutor {
    /// Predict and pre-execute likely next requests
    pub async fn speculate(&self, context: &UserContext) -> Vec<PrecomputedResult> {
        // 1. Predict likely next requests
        let predictions = self.predictor.predict_next(context, 5).await;

        // 2. Execute in background if not cached
        let mut futures = Vec::new();
        for (request, probability) in predictions {
            if probability > 0.3 && !self.is_cached(&request).await {
                futures.push(self.execute_speculative(request, probability));
            }
        }

        // 3. Store results for instant retrieval
        let results = join_all(futures).await;
        for result in &results {
            self.precomputed.store(result).await;
        }

        results
    }

    /// Check if speculative result is available
    pub async fn get_speculative(&self, request: &InferenceRequest) -> Option<InferenceResult> {
        self.precomputed.get(&request.hash()).await
    }
}

/// Request pattern predictor using ML
pub struct RequestPredictor {
    /// Sequence model for request patterns
    model: SequenceModel,
    /// User behavior history
    history: UserHistoryStore,
}

impl RequestPredictor {
    pub async fn predict_next(
        &self,
        context: &UserContext,
        count: usize,
    ) -> Vec<(InferenceRequest, f32)> {
        // Get user's recent request history
        let history = self.history.get_recent(&context.user_id, 10).await;

        // Predict next likely requests
        let predictions = self.model.predict(&history, count).await;

        predictions
            .into_iter()
            .map(|(req, prob)| (req, prob))
            .collect()
    }
}

2.3 Model Optimization Pipeline

// synor-compute/src/acceleration/optimization.rs

/// Automatic model optimization for faster inference
pub struct ModelOptimizer {
    /// Quantization engine
    quantizer: Quantizer,
    /// Pruning engine
    pruner: Pruner,
    /// Distillation engine
    distiller: Distiller,
    /// Compilation (TensorRT, etc.)
    compiler: ModelCompiler,
}

impl ModelOptimizer {
    /// Optimize model for target hardware
    pub async fn optimize(
        &self,
        model: &Model,
        target: &HardwareTarget,
        constraints: &OptimizationConstraints,
    ) -> Result<OptimizedModel, Error> {
        let mut optimized = model.clone();

        // 1. Quantization (FP32 → FP16 → INT8 → INT4)
        if constraints.allow_quantization {
            optimized = self.quantizer.quantize(
                &optimized,
                constraints.min_precision,
                constraints.max_accuracy_loss,
            ).await?;
        }

        // 2. Pruning (remove unimportant weights)
        if constraints.allow_pruning {
            optimized = self.pruner.prune(
                &optimized,
                constraints.max_sparsity,
                constraints.max_accuracy_loss,
            ).await?;
        }

        // 3. Compile for target hardware
        optimized = self.compiler.compile(&optimized, target).await?;

        Ok(optimized)
    }
}

/// Quantization levels
pub enum QuantizationLevel {
    FP32,           // Full precision (baseline)
    FP16,           // Half precision (~2x speedup)
    BF16,           // Brain float 16 (better range)
    INT8,           // 8-bit integer (~4x speedup)
    INT4,           // 4-bit integer (~8x speedup)
    FP8,            // 8-bit float (H100+)
    Mixed,          // Dynamic mixed precision
}

/// Hardware-specific compilation
pub struct ModelCompiler {
    /// TensorRT for NVIDIA
    tensorrt: TensorRtCompiler,
    /// ROCm MIGraphX for AMD
    migraphx: MiGraphXCompiler,
    /// OpenVINO for Intel
    openvino: OpenVinoCompiler,
    /// Core ML for Apple
    coreml: CoreMlCompiler,
}

impl ModelCompiler {
    pub async fn compile(
        &self,
        model: &Model,
        target: &HardwareTarget,
    ) -> Result<CompiledModel, Error> {
        match target.vendor {
            Vendor::Nvidia => self.tensorrt.compile(model, target).await,
            Vendor::Amd => self.migraphx.compile(model, target).await,
            Vendor::Intel => self.openvino.compile(model, target).await,
            Vendor::Apple => self.coreml.compile(model, target).await,
            _ => Ok(model.clone().into()),
        }
    }
}

2.4 Continuous Batching

// synor-compute/src/acceleration/batching.rs

/// Continuous batching for maximum GPU utilization
pub struct ContinuousBatcher {
    /// Request queue
    queue: RequestQueue,
    /// Active batches
    active_batches: Vec<ActiveBatch>,
    /// Batching configuration
    config: BatchConfig,
}

pub struct BatchConfig {
    /// Maximum batch size
    pub max_batch_size: usize,
    /// Maximum wait time for batching
    pub max_wait_ms: u64,
    /// Enable dynamic batching
    pub dynamic: bool,
    /// Enable iteration-level batching (for LLMs)
    pub iteration_level: bool,
}

impl ContinuousBatcher {
    /// Process requests with continuous batching
    pub async fn process(&self) -> Result<(), Error> {
        loop {
            // 1. Collect requests up to batch size or timeout
            let requests = self.queue.collect_batch(
                self.config.max_batch_size,
                self.config.max_wait_ms,
            ).await;

            if requests.is_empty() {
                continue;
            }

            // 2. Create batch
            let batch = self.create_batch(requests)?;

            // 3. Execute batch
            let results = self.execute_batch(batch).await?;

            // 4. Dispatch results to individual requests
            self.dispatch_results(results).await;
        }
    }

    /// Iteration-level batching for LLMs (vLLM-style)
    pub async fn process_iterative(&self) -> Result<(), Error> {
        let mut active_sequences: Vec<ActiveSequence> = Vec::new();

        loop {
            // 1. Add new requests to active sequences
            while active_sequences.len() < self.config.max_batch_size {
                if let Some(req) = self.queue.try_pop() {
                    active_sequences.push(ActiveSequence::new(req));
                } else {
                    break;
                }
            }

            if active_sequences.is_empty() {
                tokio::time::sleep(Duration::from_millis(1)).await;
                continue;
            }

            // 2. Run one iteration for all active sequences
            let next_tokens = self.run_iteration(&active_sequences).await?;

            // 3. Update sequences and remove completed ones
            let mut completed = Vec::new();
            for (i, (seq, token)) in active_sequences.iter_mut()
                .zip(next_tokens.iter())
                .enumerate()
            {
                seq.append_token(*token);
                if seq.is_complete() {
                    completed.push(i);
                }
            }

            // 4. Return completed sequences
            for i in completed.into_iter().rev() {
                let seq = active_sequences.remove(i);
                seq.complete().await;
            }
        }
    }
}

2.5 Speed Comparison

Optimization	Speedup Factor	Notes
Semantic caching	100-1000x	Cache hits are instant
Speculative execution	2-5x	For predictable workloads
INT8 quantization	2-4x	Minimal accuracy loss
INT4 quantization	4-8x	For LLMs with good quality
TensorRT compilation	2-5x	Hardware-specific optimization
Continuous batching	3-10x	Maximum GPU utilization
KV cache optimization	2-3x	For LLM inference
Combined	10-50x	Achievable with all optimizations

---

Part 3: Consumer Device Mesh Network

3.1 Universal Device Support

// synor-compute/src/mesh/device.rs

/// Any device that can contribute compute
pub enum DeviceType {
    /// Data center GPU (NVIDIA A100, H100)
    DataCenterGpu {
        model: GpuModel,
        vram_gb: u32,
        tensor_cores: u32,
    },
    /// Consumer GPU (RTX 3090, 4090)
    ConsumerGpu {
        model: GpuModel,
        vram_gb: u32,
    },
    /// Mobile device (phone, tablet)
    Mobile {
        platform: MobilePlatform,
        chip: MobileChip,
        gpu: MobileGpu,
    },
    /// Desktop/Laptop CPU
    Cpu {
        vendor: CpuVendor,
        cores: u32,
        threads: u32,
        avx_support: AvxSupport,
    },
    /// Browser (WebGPU/WebAssembly)
    Browser {
        runtime: BrowserRuntime,
        gpu_available: bool,
        wasm_simd: bool,
    },
    /// Apple Silicon (M1, M2, M3)
    AppleSilicon {
        chip: AppleChip,
        gpu_cores: u32,
        neural_engine_cores: u32,
        unified_memory_gb: u32,
    },
    /// TPU (if accessible)
    Tpu {
        version: TpuVersion,
        chips: u32,
    },
    /// Custom accelerator (Groq LPU, Cerebras, etc.)
    CustomAccelerator {
        vendor: String,
        model: String,
        tops: f32,  // Tera operations per second
    },
}

pub enum MobilePlatform {
    Ios,
    Android,
}

pub enum MobileChip {
    // Apple
    A15Bionic,
    A16Bionic,
    A17Pro,
    // Qualcomm
    Snapdragon8Gen1,
    Snapdragon8Gen2,
    Snapdragon8Gen3,
    // Samsung
    Exynos2200,
    Exynos2400,
    // Google
    Tensor,
    TensorG2,
    TensorG3,
    // MediaTek
    Dimensity9000,
    Dimensity9300,
}

pub enum MobileGpu {
    // Apple
    AppleGpu { cores: u32 },
    // Qualcomm
    Adreno { model: u32 },
    // ARM
    MaliG { model: u32 },
    // IMG
    PowerVR { model: String },
}

3.2 Device Capability Registry

// synor-compute/src/mesh/registry.rs

/// Central registry of all contributing devices
pub struct DeviceRegistry {
    /// All registered devices
    devices: HashMap<DeviceId, DeviceInfo>,
    /// Devices by capability
    by_capability: CapabilityIndex,
    /// Devices by location
    by_location: GeoIndex,
    /// Device reputation scores
    reputation: ReputationStore,
}

/// Detailed device capabilities
pub struct DeviceInfo {
    pub device_id: DeviceId,
    pub device_type: DeviceType,
    pub owner: Address,
    /// Compute capabilities
    pub compute: ComputeCapabilities,
    /// Network capabilities
    pub network: NetworkCapabilities,
    /// Availability schedule
    pub availability: AvailabilitySchedule,
    /// Current status
    pub status: DeviceStatus,
    /// Reputation score (0-100)
    pub reputation: u32,
}

pub struct ComputeCapabilities {
    /// FLOPS (single precision)
    pub fp32_gflops: f64,
    /// FLOPS (half precision)
    pub fp16_gflops: f64,
    /// Integer operations
    pub int8_tops: f64,
    /// Memory bandwidth (GB/s)
    pub memory_bandwidth: f64,
    /// Available memory (GB)
    pub memory_gb: f64,
    /// Supported frameworks
    pub frameworks: Vec<Framework>,
    /// Supported model formats
    pub model_formats: Vec<ModelFormat>,
}

pub struct NetworkCapabilities {
    /// Download speed (Mbps)
    pub download_mbps: f64,
    /// Upload speed (Mbps)
    pub upload_mbps: f64,
    /// Latency to nearest edge (ms)
    pub edge_latency_ms: u32,
    /// NAT type
    pub nat_type: NatType,
}

/// When device is available
pub struct AvailabilitySchedule {
    /// Always available
    pub always: bool,
    /// Available hours (UTC)
    pub hours: Option<Vec<(u8, u8)>>,
    /// Available only when idle
    pub idle_only: bool,
    /// Available only when charging (mobile)
    pub charging_only: bool,
    /// Minimum battery level (mobile)
    pub min_battery: Option<u8>,
}

3.3 Mobile SDK

// synor-compute/src/sdk/mobile.rs

/// Mobile SDK for contributing compute
pub struct SynorMobileSDK {
    /// Device identifier
    device_id: DeviceId,
    /// User wallet
    wallet: Wallet,
    /// Local inference runtime
    runtime: MobileInferenceRuntime,
    /// Task queue
    tasks: TaskQueue,
    /// Earnings tracker
    earnings: EarningsTracker,
}

impl SynorMobileSDK {
    /// Initialize SDK
    pub async fn init(config: MobileConfig) -> Result<Self, Error> {
        // 1. Generate or load device ID
        let device_id = Self::get_or_create_device_id().await?;

        // 2. Initialize wallet
        let wallet = Wallet::load_or_create(&config.keystore_path).await?;

        // 3. Detect device capabilities
        let capabilities = Self::detect_capabilities().await?;

        // 4. Initialize inference runtime
        let runtime = MobileInferenceRuntime::new(&capabilities)?;

        // 5. Register with network
        Self::register_device(&device_id, &capabilities).await?;

        Ok(Self {
            device_id,
            wallet,
            runtime,
            tasks: TaskQueue::new(),
            earnings: EarningsTracker::new(),
        })
    }

    /// Start contributing compute
    pub async fn start_contributing(&self, settings: ContributionSettings) -> Result<(), Error> {
        loop {
            // 1. Check if we should be active
            if !self.should_be_active(&settings).await {
                tokio::time::sleep(Duration::from_secs(60)).await;
                continue;
            }

            // 2. Get available tasks
            let task = self.get_next_task().await?;

            // 3. Execute task
            let result = self.execute_task(&task).await?;

            // 4. Submit result and earn rewards
            let reward = self.submit_result(&task, &result).await?;
            self.earnings.add(reward);
        }
    }

    /// Check contribution conditions
    async fn should_be_active(&self, settings: &ContributionSettings) -> bool {
        // Check battery
        if let Some(min_battery) = settings.min_battery {
            if Self::battery_level() < min_battery {
                return false;
            }
        }

        // Check if charging
        if settings.charging_only && !Self::is_charging() {
            return false;
        }

        // Check if idle
        if settings.idle_only && !Self::is_idle() {
            return false;
        }

        // Check thermal state
        if Self::thermal_state() == ThermalState::Critical {
            return false;
        }

        true
    }
}

/// Mobile inference runtime
pub struct MobileInferenceRuntime {
    /// Core ML for iOS
    #[cfg(target_os = "ios")]
    coreml: CoreMlRuntime,
    /// NNAPI/GPU delegate for Android
    #[cfg(target_os = "android")]
    tflite: TfLiteRuntime,
    /// Metal for Apple GPUs
    #[cfg(any(target_os = "ios", target_os = "macos"))]
    metal: MetalRuntime,
    /// OpenCL for Android GPUs
    #[cfg(target_os = "android")]
    opencl: OpenClRuntime,
}

3.4 Browser SDK (WebGPU + WASM)

// synor-compute/sdk/browser/src/index.ts

/**
 * Browser SDK for contributing compute via WebGPU/WASM
 */
export class SynorBrowserSDK {
  private deviceId: string;
  private wallet: BrowserWallet;
  private runtime: BrowserRuntime;
  private webgpu: GPUDevice | null;
  private worker: Worker;

  /**
   * Initialize SDK in browser
   */
  static async init(config: BrowserConfig): Promise<SynorBrowserSDK> {
    const sdk = new SynorBrowserSDK();

    // 1. Check WebGPU support
    if (navigator.gpu) {
      const adapter = await navigator.gpu.requestAdapter();
      if (adapter) {
        sdk.webgpu = await adapter.requestDevice();
        console.log('WebGPU available');
      }
    }

    // 2. Initialize WASM runtime
    sdk.runtime = await BrowserRuntime.init();

    // 3. Create/load wallet
    sdk.wallet = await BrowserWallet.loadOrCreate();

    // 4. Generate device ID
    sdk.deviceId = await sdk.generateDeviceId();

    // 5. Start worker thread
    sdk.worker = new Worker(new URL('./worker.ts', import.meta.url));

    return sdk;
  }

  /**
   * Start contributing compute
   */
  async startContributing(settings: ContributionSettings): Promise<void> {
    // Register capabilities
    const capabilities = await this.detectCapabilities();
    await this.registerDevice(capabilities);

    // Start task loop in worker
    this.worker.postMessage({
      type: 'start',
      settings,
      capabilities,
    });

    // Listen for results
    this.worker.onmessage = async (event) => {
      if (event.data.type === 'task_complete') {
        await this.submitResult(event.data.taskId, event.data.result);
      } else if (event.data.type === 'earnings_update') {
        this.onEarningsUpdate?.(event.data.earnings);
      }
    };
  }

  /**
   * Detect browser compute capabilities
   */
  private async detectCapabilities(): Promise<BrowserCapabilities> {
    return {
      // WebGPU
      webgpu: {
        available: !!this.webgpu,
        maxBufferSize: this.webgpu?.limits.maxBufferSize,
        maxComputeWorkgroupSizeX: this.webgpu?.limits.maxComputeWorkgroupSizeX,
      },
      // WASM
      wasm: {
        simd: await this.checkWasmSimd(),
        threads: await this.checkWasmThreads(),
        memory64: await this.checkWasmMemory64(),
      },
      // Hardware
      hardwareConcurrency: navigator.hardwareConcurrency,
      deviceMemory: (navigator as any).deviceMemory,
      // Network
      connection: (navigator as any).connection,
    };
  }

  /**
   * Execute inference task using WebGPU
   */
  private async executeWithWebGPU(task: InferenceTask): Promise<InferenceResult> {
    // Load model if not cached
    if (!this.modelCache.has(task.modelId)) {
      const model = await this.loadModel(task.modelId);
      this.modelCache.set(task.modelId, model);
    }

    const model = this.modelCache.get(task.modelId)!;

    // Create input buffer
    const inputBuffer = this.webgpu!.createBuffer({
      size: task.input.byteLength,
      usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST,
    });
    this.webgpu!.queue.writeBuffer(inputBuffer, 0, task.input);

    // Execute compute shader
    const commandEncoder = this.webgpu!.createCommandEncoder();
    const passEncoder = commandEncoder.beginComputePass();
    passEncoder.setPipeline(model.pipeline);
    passEncoder.setBindGroup(0, model.bindGroup);
    passEncoder.dispatchWorkgroups(
      Math.ceil(task.input.length / 256)
    );
    passEncoder.end();

    // Read results
    const outputBuffer = this.webgpu!.createBuffer({
      size: model.outputSize,
      usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST,
    });
    commandEncoder.copyBufferToBuffer(
      model.outputBuffer, 0,
      outputBuffer, 0,
      model.outputSize
    );

    this.webgpu!.queue.submit([commandEncoder.finish()]);

    await outputBuffer.mapAsync(GPUMapMode.READ);
    const result = new Float32Array(outputBuffer.getMappedRange());
    outputBuffer.unmap();

    return { output: result };
  }
}

3.5 Desktop App SDK

// synor-compute/src/sdk/desktop.rs

/// Desktop SDK for contributing compute
pub struct SynorDesktopSDK {
    device_id: DeviceId,
    wallet: Wallet,
    /// GPU runtime (CUDA, ROCm, Metal, etc.)
    gpu_runtime: Option<GpuRuntime>,
    /// CPU runtime
    cpu_runtime: CpuRuntime,
    /// Task scheduler
    scheduler: LocalScheduler,
    /// System monitor
    monitor: SystemMonitor,
}

impl SynorDesktopSDK {
    pub async fn init() -> Result<Self, Error> {
        // 1. Detect all available compute resources
        let gpus = GpuDetector::detect_all().await?;
        let cpus = CpuDetector::detect().await?;

        // 2. Initialize runtimes
        let gpu_runtime = if !gpus.is_empty() {
            Some(GpuRuntime::init(&gpus).await?)
        } else {
            None
        };

        let cpu_runtime = CpuRuntime::init(&cpus).await?;

        // 3. Start system monitor
        let monitor = SystemMonitor::new();

        Ok(Self {
            device_id: DeviceId::generate(),
            wallet: Wallet::load_or_create().await?,
            gpu_runtime,
            cpu_runtime,
            scheduler: LocalScheduler::new(),
            monitor,
        })
    }

    /// Configure resource sharing
    pub fn configure(&mut self, config: DesktopContributionConfig) {
        // How much GPU to share (0-100%)
        self.scheduler.set_gpu_limit(config.gpu_share_percent);

        // How much CPU to share
        self.scheduler.set_cpu_limit(config.cpu_share_percent);

        // How much memory to share
        self.scheduler.set_memory_limit(config.memory_share_percent);

        // Only run when idle
        self.scheduler.set_idle_only(config.idle_only);

        // Power mode preferences
        self.scheduler.set_power_mode(config.power_mode);
    }
}

/// GPU detection for all platforms
pub struct GpuDetector;

impl GpuDetector {
    pub async fn detect_all() -> Result<Vec<GpuInfo>, Error> {
        let mut gpus = Vec::new();

        // NVIDIA (CUDA)
        #[cfg(feature = "cuda")]
        gpus.extend(Self::detect_nvidia().await?);

        // AMD (ROCm)
        #[cfg(feature = "rocm")]
        gpus.extend(Self::detect_amd().await?);

        // Intel (OneAPI)
        #[cfg(feature = "oneapi")]
        gpus.extend(Self::detect_intel().await?);

        // Apple (Metal)
        #[cfg(target_os = "macos")]
        gpus.extend(Self::detect_apple().await?);

        Ok(gpus)
    }

    #[cfg(feature = "cuda")]
    async fn detect_nvidia() -> Result<Vec<GpuInfo>, Error> {
        use nvml_wrapper::Nvml;

        let nvml = Nvml::init()?;
        let device_count = nvml.device_count()?;

        let mut gpus = Vec::new();
        for i in 0..device_count {
            let device = nvml.device_by_index(i)?;
            gpus.push(GpuInfo {
                vendor: GpuVendor::Nvidia,
                name: device.name()?,
                vram_bytes: device.memory_info()?.total,
                compute_capability: device.cuda_compute_capability()?,
                driver_version: nvml.sys_driver_version()?,
            });
        }

        Ok(gpus)
    }
}

3.6 Contribution Rewards Model

// synor-compute/src/economics/rewards.rs

/// Reward calculation for device contributors
pub struct RewardCalculator {
    /// Base reward rates
    base_rates: BaseRates,
    /// Reputation multiplier
    reputation_multiplier: ReputationMultiplier,
    /// Uptime bonuses
    uptime_bonus: UptimeBonus,
}

pub struct BaseRates {
    /// Per TFLOP-second (GPU)
    pub gpu_tflops: f64,      // 0.000001 SYNOR/TFLOP-s
    /// Per GFLOP-second (CPU)
    pub cpu_gflops: f64,      // 0.00000001 SYNOR/GFLOP-s
    /// Per GB transferred
    pub bandwidth_gb: f64,    // 0.001 SYNOR/GB
    /// Per hour of availability
    pub availability_hour: f64, // 0.0001 SYNOR/hour
}

impl RewardCalculator {
    pub fn calculate_reward(&self, contribution: &Contribution) -> Reward {
        let base = match contribution.resource_type {
            ResourceType::Gpu => {
                contribution.tflops * contribution.duration.as_secs_f64()
                    * self.base_rates.gpu_tflops
            }
            ResourceType::Cpu => {
                contribution.gflops * contribution.duration.as_secs_f64()
                    * self.base_rates.cpu_gflops
            }
            ResourceType::Bandwidth => {
                contribution.gb_transferred * self.base_rates.bandwidth_gb
            }
        };

        // Apply reputation multiplier (0.5x to 2x)
        let reputation_mult = self.reputation_multiplier.get(contribution.reputation);

        // Apply uptime bonus (up to 20% extra)
        let uptime_mult = self.uptime_bonus.get(contribution.uptime_percent);

        Reward {
            base,
            reputation_bonus: base * (reputation_mult - 1.0),
            uptime_bonus: base * (uptime_mult - 1.0),
            total: base * reputation_mult * uptime_mult,
        }
    }
}

/// Expected monthly earnings by device type
pub struct EarningsEstimator;

impl EarningsEstimator {
    pub fn estimate_monthly(device: &DeviceType, hours_per_day: f64) -> MonthlyEarnings {
        let hourly = match device {
            DeviceType::DataCenterGpu { .. } => 0.50,    // $0.50/hour
            DeviceType::ConsumerGpu { .. } => 0.10,      // $0.10/hour
            DeviceType::AppleSilicon { .. } => 0.05,     // $0.05/hour
            DeviceType::Cpu { .. } => 0.01,              // $0.01/hour
            DeviceType::Mobile { .. } => 0.005,          // $0.005/hour
            DeviceType::Browser { .. } => 0.002,         // $0.002/hour
            _ => 0.01,
        };

        let daily = hourly * hours_per_day;
        let monthly = daily * 30.0;

        MonthlyEarnings {
            low: monthly * 0.5,   // 50% utilization
            medium: monthly * 0.7, // 70% utilization
            high: monthly,         // 100% utilization
        }
    }
}

---

Part 4: Task Distribution Algorithm

4.1 Optimal Task Router

// synor-compute/src/scheduler/router.rs

/// Routes tasks to optimal devices
pub struct TaskRouter {
    /// Device registry
    devices: Arc<DeviceRegistry>,
    /// Cost optimizer
    cost_optimizer: CostOptimizer,
    /// Latency optimizer
    latency_optimizer: LatencyOptimizer,
    /// Load balancer
    load_balancer: LoadBalancer,
}

impl TaskRouter {
    /// Find optimal device(s) for task
    pub async fn route(&self, task: &ComputeTask) -> Result<RoutingDecision, Error> {
        // 1. Filter devices that can handle this task
        let capable_devices = self.devices
            .find_capable(&task.requirements)
            .await?;

        // 2. Score each device
        let mut scored: Vec<(DeviceId, RoutingScore)> = Vec::new();

        for device in capable_devices {
            let score = self.score_device(&device, task).await?;
            scored.push((device.device_id, score));
        }

        // 3. Sort by composite score
        scored.sort_by(|a, b| b.1.composite.partial_cmp(&a.1.composite).unwrap());

        // 4. Select best device(s)
        let selected = if task.distributed {
            // Select multiple devices for distributed task
            self.select_distributed(&scored, task)
        } else {
            // Single best device
            vec![scored[0].0.clone()]
        };

        Ok(RoutingDecision {
            devices: selected,
            estimated_cost: scored[0].1.cost,
            estimated_latency: scored[0].1.latency,
            estimated_duration: scored[0].1.duration,
        })
    }

    async fn score_device(&self, device: &DeviceInfo, task: &ComputeTask) -> Result<RoutingScore, Error> {
        // Cost score (lower is better)
        let cost = self.cost_optimizer.estimate_cost(device, task);
        let cost_score = 1.0 / (1.0 + cost);

        // Latency score (lower is better)
        let latency = self.latency_optimizer.estimate_latency(device, task);
        let latency_score = 1.0 / (1.0 + latency.as_millis() as f64 / 1000.0);

        // Capability score (higher compute = better)
        let capability_score = device.compute.fp16_gflops / task.requirements.min_gflops;

        // Reputation score
        let reputation_score = device.reputation as f64 / 100.0;

        // Load score (less loaded = better)
        let load = self.load_balancer.current_load(&device.device_id).await?;
        let load_score = 1.0 - load;

        // Composite score with weights
        let composite =
            cost_score * 0.3 +
            latency_score * 0.2 +
            capability_score * 0.2 +
            reputation_score * 0.15 +
            load_score * 0.15;

        Ok(RoutingScore {
            cost,
            latency,
            duration: self.estimate_duration(device, task),
            composite,
        })
    }
}

4.2 Distributed Task Sharding

// synor-compute/src/scheduler/sharding.rs

/// Shard large tasks across multiple devices
pub struct TaskSharder {
    /// Sharding strategies
    strategies: HashMap<TaskType, Box<dyn ShardingStrategy>>,
}

#[async_trait]
pub trait ShardingStrategy: Send + Sync {
    /// Shard task into subtasks
    async fn shard(&self, task: &ComputeTask, devices: &[DeviceInfo]) -> Result<Vec<Shard>, Error>;

    /// Aggregate results from shards
    async fn aggregate(&self, results: Vec<ShardResult>) -> Result<TaskResult, Error>;
}

/// Data parallel sharding (same model, different data)
pub struct DataParallelSharder;

#[async_trait]
impl ShardingStrategy for DataParallelSharder {
    async fn shard(&self, task: &ComputeTask, devices: &[DeviceInfo]) -> Result<Vec<Shard>, Error> {
        let data_size = task.input_data.len();
        let num_shards = devices.len();
        let shard_size = data_size / num_shards;

        let mut shards = Vec::new();
        for (i, device) in devices.iter().enumerate() {
            let start = i * shard_size;
            let end = if i == num_shards - 1 { data_size } else { start + shard_size };

            shards.push(Shard {
                shard_id: i as u32,
                device_id: device.device_id.clone(),
                model: task.model.clone(),
                data_range: start..end,
            });
        }

        Ok(shards)
    }

    async fn aggregate(&self, results: Vec<ShardResult>) -> Result<TaskResult, Error> {
        // Concatenate results in order
        let mut aggregated = Vec::new();
        for result in results.into_iter().sorted_by_key(|r| r.shard_id) {
            aggregated.extend(result.output);
        }
        Ok(TaskResult { output: aggregated })
    }
}

/// Model parallel sharding (different model layers on different devices)
pub struct ModelParallelSharder {
    /// Layer assignments
    layer_assignments: Vec<(usize, usize)>,  // (start_layer, end_layer)
}

#[async_trait]
impl ShardingStrategy for ModelParallelSharder {
    async fn shard(&self, task: &ComputeTask, devices: &[DeviceInfo]) -> Result<Vec<Shard>, Error> {
        // Assign model layers to devices based on memory
        let total_layers = task.model.num_layers();
        let mut assignments = Vec::new();
        let mut current_layer = 0;

        for device in devices {
            let layers_for_device = self.calculate_layers_for_device(
                device,
                &task.model,
                total_layers - current_layer,
            );

            assignments.push(Shard {
                shard_id: assignments.len() as u32,
                device_id: device.device_id.clone(),
                model_layers: current_layer..(current_layer + layers_for_device),
                data: task.input_data.clone(),
            });

            current_layer += layers_for_device;
        }

        Ok(assignments)
    }

    async fn aggregate(&self, results: Vec<ShardResult>) -> Result<TaskResult, Error> {
        // Pipeline results through layers
        // Last shard result is the final output
        Ok(results.into_iter().last().unwrap().into())
    }
}

---

Summary: Achieving 90% Cost Reduction + 10x Speed

Cost Reduction Breakdown

Factor	Savings	How
Zero cloud margin	35%	Protocol-only, no corporate overhead
Distributed infra	15%	No data center costs
Spot market	20%	Fill idle capacity at discount
Geo arbitrage	10%	Route to cheap electricity
Consumer devices	10%	Free idle compute
Total	90%	Combined savings

Speed Improvement Breakdown

Optimization	Speedup	How
Semantic caching	10-100x	Reuse similar results
Speculative execution	2-5x	Pre-compute likely requests
Quantization (INT4/INT8)	4-8x	Reduced precision inference
Hardware compilation	2-5x	TensorRT, custom kernels
Continuous batching	3-10x	Maximum GPU utilization
Edge compute	2-5x	Compute closer to user
Combined	10-50x	With all optimizations

Consumer Device Contribution

Device Type	Contribution	Monthly Earnings
Data center GPU	Full training/inference	$100-500
Consumer GPU	Inference, light training	$30-100
Apple Silicon	Efficient inference	$15-50
Desktop CPU	Data processing, embeddings	$5-20
Mobile device	Edge inference	$2-10
Browser	Light compute, idle cycles	$1-5

This architecture creates a truly decentralized compute network that can undercut traditional cloud providers while providing competitive performance.