Architecture Overview

Technical architecture and design of Bud Runtime

System Architecture

Bud Runtime is built on a microservices architecture designed for scalability, reliability, and performance.

Core Components

Component Details

API Gateway

The API Gateway serves as the single entry point for all client requests:

• Load Balancing: Distributes requests across model servers
• Authentication: Validates API keys and JWT tokens
• Rate Limiting: Enforces usage quotas
• Request Routing: Routes to appropriate model servers

Model Servers

Model servers handle the actual inference workloads:

• Model Loading: Efficient loading and caching of models
• Batch Processing: Groups requests for better throughput
• GPU Management: Optimal GPU memory utilization
• Health Monitoring: Regular health checks and auto-recovery

Storage Layer

Storage Components:
  Model Repository:
    - Type: Object Storage (S3-compatible)
    - Purpose: Central model storage
    - Features: Versioning, replication

  Cache Layer:
    - Type: Distributed cache (Redis)
    - Purpose: Fast model weight access
    - Features: LRU eviction, persistence

  Metadata Store:
    - Type: PostgreSQL
    - Purpose: Model metadata, configurations
    - Features: ACID compliance, replication

Request Flow

Text Generation Request

1. Client Request: Client sends request to API Gateway
2. Authentication: Gateway validates credentials
3. Rate Limiting: Checks against usage quotas
4. Routing: Determines optimal model server
5. Model Loading: Server loads model if not cached
6. Inference: Processes request on GPU
7. Response: Returns generated text to client

Client->Gateway: POST /v1/completions
Gateway->Auth: Validate token
Auth->Gateway: Token valid
Gateway->Router: Route request
Router->ModelServer: Forward request
ModelServer->GPU: Run inference
GPU->ModelServer: Return output
ModelServer->Gateway: Send response
Gateway->Client: Return completion

Image Generation Request

Similar flow with additional steps:

1. Preprocessing: Image prompt processing
2. Model Selection: Choose appropriate image model
3. Generation: Multi-step diffusion process
4. Post-processing: Image encoding and storage
5. URL Generation: Create accessible image URL

Scaling Architecture

Horizontal Scaling

Scaling Strategy:
  Model Servers:
    - Auto-scaling based on GPU utilization
    - Min replicas: 1
    - Max replicas: 100
    - Target GPU utilization: 70%

  API Gateway:
    - CPU-based auto-scaling
    - Min replicas: 2
    - Max replicas: 20
    - Target CPU: 60%

Vertical Scaling

GPU Tiers:
  Small Models:
    - GPU: NVIDIA T4
    - Memory: 16GB
    - Models: 7B parameters

  Medium Models:
    - GPU: NVIDIA A10
    - Memory: 24GB
    - Models: 13B parameters

  Large Models:
    - GPU: NVIDIA A100
    - Memory: 80GB
    - Models: 70B+ parameters

High Availability

Redundancy

• Multi-zone deployment: Spread across availability zones
• Model replication: Multiple copies of each model
• Database replication: Primary-replica setup
• Gateway redundancy: Multiple gateway instances

Failover Strategy

Failover Configuration:
  Health Checks:
    - Interval: 10s
    - Timeout: 5s
    - Threshold: 3 failures

  Recovery:
    - Automatic pod restart
    - Node evacuation on failure
    - Cross-zone failover
    - Circuit breaker pattern

Performance Architecture

Caching Strategy

class ModelCache:
    """Multi-level caching for model weights."""

    def __init__(self):
        self.l1_cache = {}  # In-memory cache
        self.l2_cache = RedisCache()  # Distributed cache
        self.l3_cache = S3Storage()  # Persistent storage

    async def get_model(self, model_id: str) -> Model:
        # Try L1 cache first
        if model_id in self.l1_cache:
            return self.l1_cache[model_id]

        # Try L2 cache
        model = await self.l2_cache.get(model_id)
        if model:
            self.l1_cache[model_id] = model
            return model

        # Load from L3 storage
        model = await self.l3_cache.load(model_id)
        await self.l2_cache.set(model_id, model)
        self.l1_cache[model_id] = model
        return model

Batch Processing

type BatchProcessor struct {
    batchSize    int
    batchTimeout time.Duration
    queue        chan Request
    processor    ModelProcessor
}

func (b *BatchProcessor) Process() {
    batch := make([]Request, 0, b.batchSize)
    timer := time.NewTimer(b.batchTimeout)

    for {
        select {
        case req := <-b.queue:
            batch = append(batch, req)
            if len(batch) >= b.batchSize {
                b.processBatch(batch)
                batch = batch[:0]
                timer.Reset(b.batchTimeout)
            }
        case <-timer.C:
            if len(batch) > 0 {
                b.processBatch(batch)
                batch = batch[:0]
            }
            timer.Reset(b.batchTimeout)
        }
    }
}

Security Architecture

Network Security

Network Policies:
  Ingress:
    - Allow from Load Balancer only
    - TLS 1.3 minimum
    - Certificate pinning

  Egress:
    - Model repository access
    - Metrics endpoints
    - No external internet access

  Inter-service:
    - mTLS between services
    - Service mesh (Istio)
    - Network segmentation

Data Security

• Encryption at rest: All stored data encrypted
• Encryption in transit: TLS for all communications
• Key management: Integrated with KMS
• Access control: RBAC with fine-grained permissions

Monitoring Architecture

Metrics Collection

Metrics Stack:
  Collection:
    - Prometheus for metrics
    - OpenTelemetry for traces
    - Fluentd for logs

  Storage:
    - Thanos for long-term metrics
    - Elasticsearch for logs
    - Jaeger for traces

  Visualization:
    - Grafana dashboards
    - Custom monitoring UI
    - Alert manager

Key Metrics

# System Metrics
- GPU Utilization: gpu_utilization_percent
- Memory Usage: container_memory_usage_bytes
- Request Latency: http_request_duration_seconds
- Error Rate: http_requests_total{status=~"5.."}

# Business Metrics
- Tokens Generated: tokens_generated_total
- Model Usage: model_requests_total{model}
- User Activity: active_users_daily
- Cost per Request: request_cost_dollars

Development Architecture

CI/CD Pipeline

Pipeline Stages:
  Build:
    - Code compilation
    - Docker image creation
    - Security scanning

  Test:
    - Unit tests
    - Integration tests
    - Performance tests

  Deploy:
    - Staging deployment
    - Smoke tests
    - Production rollout
    - Health verification

Development Environment

Dev Setup:
  Local:
    - Kind/Minikube cluster
    - Mock GPU support
    - Local model cache

  Staging:
    - Full cluster replica
    - Subset of models
    - Performance testing

  Production:
    - Multi-region deployment
    - Full model catalog
    - High availability

For implementation guidelines, see Development Guidelines.