06-distributed-inference.md

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title	vLLM Tutorial - Chapter 6: Distributed Inference
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parent	vLLM Tutorial

Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes

Welcome to Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes. In this part of vLLM Tutorial: High-Performance LLM Inference, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

Master distributed inference techniques with vLLM including multi-GPU setups, tensor parallelism, and cluster deployments.

Overview

Distributed inference enables scaling vLLM across multiple GPUs and compute nodes. This chapter covers tensor parallelism, pipeline parallelism, multi-node setups, and production deployment strategies for handling high-throughput workloads.

Tensor Parallelism Basics

Single-Node Multi-GPU Setup

from vllm import LLM, SamplingParams
import torch

def setup_tensor_parallelism(model_name, num_gpus=2):
    """Set up tensor parallelism across multiple GPUs on single node"""

    print(f"Setting up tensor parallelism with {num_gpus} GPUs...")

    # Check GPU availability
    available_gpus = torch.cuda.device_count()
    if available_gpus < num_gpus:
        raise ValueError(f"Requested {num_gpus} GPUs but only {available_gpus} available")

    try:
        llm = LLM(
            model=model_name,
            tensor_parallel_size=num_gpus,  # Enable tensor parallelism
            gpu_memory_utilization=0.85,   # Use 85% of each GPU's memory
            max_model_len=2048,
            dtype="half"  # FP16 for memory efficiency
        )

        print(f"✅ Tensor parallelism configured across {num_gpus} GPUs")
        return llm

    except Exception as e:
        print(f"❌ Tensor parallelism setup failed: {e}")
        return None

# Test tensor parallelism
if torch.cuda.device_count() >= 2:
    tp_llm = setup_tensor_parallelism("microsoft/DialoGPT-large", num_gpus=2)

    if tp_llm:
        # Test generation
        prompts = ["Explain quantum computing", "What is machine learning?"]
        results = tp_llm.generate(prompts, SamplingParams(max_tokens=100))

        print("Tensor parallelism test results:")
        for i, result in enumerate(results):
            print(f"Prompt {i+1}: {len(result.outputs[0].text)} characters generated")
else:
    print("Multi-GPU setup not available - requires 2+ GPUs")

Understanding Tensor Parallelism

# Visualize tensor parallelism
def explain_tensor_parallelism():
    """Explain how tensor parallelism works"""

    print("Tensor Parallelism Explanation:")
    print("=" * 40)

    print("\nWithout Tensor Parallelism:")
    print("Single GPU handles entire model")
    print("Model: [Layer1][Layer2][Layer3][Layer4]")
    print("GPU 1: [L1][L2][L3][L4] ← Limited by GPU memory")

    print("\nWith Tensor Parallelism (2 GPUs):")
    print("Model split across GPUs")
    print("GPU 1: [L1][L2]     GPU 2: [L3][L4]")
    print("Communication overhead but 2x memory capacity")

    print("\nBenefits:")
    print("• Scale to larger models")
    print("• Better memory utilization")
    print("• Higher throughput")

    print("\nTrade-offs:")
    print("• Inter-GPU communication latency")
    print("• More complex setup")
    print("• Not all operations are parallelizable")

explain_tensor_parallelism()

Performance Scaling with Tensor Parallelism

def benchmark_tensor_parallelism(model_name="microsoft/DialoGPT-large"):
    """Benchmark performance scaling with tensor parallelism"""

    import time

    results = {}

    # Test different GPU counts
    gpu_counts = [1] + list(range(2, torch.cuda.device_count() + 1, 2))

    test_prompts = ["Write a detailed explanation of neural networks"] * 4
    sampling_params = SamplingParams(max_tokens=200, temperature=0.7)

    for num_gpus in gpu_counts:
        try:
            print(f"\nBenchmarking with {num_gpus} GPUs...")

            # Setup
            llm = LLM(
                model=model_name,
                tensor_parallel_size=num_gpus,
                gpu_memory_utilization=0.8
            )

            # Warm up
            llm.generate(["warmup"], SamplingParams(max_tokens=10))

            # Benchmark
            start_time = time.time()
            batch_results = llm.generate(test_prompts, sampling_params)
            end_time = time.time()

            batch_time = end_time - start_time
            total_tokens = sum(len(r.outputs[0].token_ids) for r in batch_results)
            throughput = total_tokens / batch_time

            results[num_gpus] = {
                'throughput': throughput,
                'batch_time': batch_time,
                'total_tokens': total_tokens,
                'success': True
            }

            print(".2f")
            print(".3f")

        except Exception as e:
            results[num_gpus] = {
                'success': False,
                'error': str(e)
            }
            print(f"❌ Failed: {e}")

    return results

# Run benchmark if multi-GPU available
if torch.cuda.device_count() >= 2:
    tp_results = benchmark_tensor_parallelism()

    print("\nTensor Parallelism Scaling Results:")
    print("=" * 50)

    for gpus, result in tp_results.items():
        if result['success']:
            print(f"{gpus} GPUs: {result['throughput']:.2f} tokens/s")
        else:
            print(f"{gpus} GPUs: Failed - {result['error']}")
else:
    print("Multi-GPU benchmark requires 2+ GPUs")

Pipeline Parallelism

Model Parallelism Strategies

# Pipeline parallelism (advanced - requires custom setup)
def setup_pipeline_parallelism():
    """Set up pipeline parallelism across GPUs"""

    print("Pipeline Parallelism Setup:")
    print("Note: This is a conceptual demonstration")
    print("Actual implementation requires model surgery and custom distribution")

    # Conceptual pipeline setup
    pipeline_config = {
        'gpu_0': ['embedding', 'layer_0', 'layer_1'],
        'gpu_1': ['layer_2', 'layer_3', 'layer_4'],
        'gpu_2': ['layer_5', 'layer_6', 'layer_7'],
        'gpu_3': ['layer_8', 'layer_9', 'output']
    }

    print("\nPipeline Distribution:")
    for gpu, layers in pipeline_config.items():
        print(f"{gpu}: {layers}")

    print("\nBenefits:")
    print("• Micro-batch processing")
    print("• Reduced memory per GPU")
    print("• Better for deep models")

    print("\nChallenges:")
    print("• Pipeline bubble time")
    print("• Complex synchronization")
    print("• Harder to implement")

    return pipeline_config

pipeline_config = setup_pipeline_parallelism()

3D Parallelism (Data + Tensor + Pipeline)

def explain_3d_parallelism():
    """Explain 3D parallelism strategy"""

    print("3D Parallelism: Data + Tensor + Pipeline")
    print("=" * 50)

    print("\nData Parallelism:")
    print("• Same model on multiple GPUs")
    print("• Different data batches per GPU")
    print("• Gradient synchronization")
    print("• Scales batch size")

    print("\nTensor Parallelism:")
    print("• Model split across GPUs")
    print("• Parallel matrix operations")
    print("• Scales model size")
    print("• Requires fast interconnect")

    print("\nPipeline Parallelism:")
    print("• Model layers distributed across GPUs")
    print("• Micro-batch pipelining")
    print("• Scales model depth")
    print("• Minimizes memory per GPU")

    print("\nCombined 3D Parallelism:")
    print("• Data: Multiple replicas")
    print("• Tensor: Split within replicas")
    print("• Pipeline: Layers across GPUs")
    print("• Maximum scalability")

    print("\nExample: 8 GPUs, 2 nodes")
    print("Node 1: [Data1-Tensor1-Pipeline1] [Data2-Tensor2-Pipeline2]")
    print("Node 2: [Data3-Tensor3-Pipeline3] [Data4-Tensor4-Pipeline4]")

explain_3d_parallelism()

Multi-Node Distributed Inference

Ray-Based Distributed Setup

# Multi-node setup with Ray (if available)
try:
    import ray

    def setup_ray_cluster():
        """Set up Ray cluster for distributed inference"""

        # Initialize Ray
        ray.init()

        @ray.remote(num_gpus=1)
        class DistributedLLM:
            def __init__(self, model_name):
                from vllm import LLM
                self.llm = LLM(model_name)

            def generate(self, prompts, sampling_params):
                return self.llm.generate(prompts, sampling_params)

        # Create distributed LLM instances
        llm_actors = [
            DistributedLLM.remote("microsoft/DialoGPT-medium")
            for _ in range(min(4, torch.cuda.device_count()))
        ]

        print(f"✅ Created {len(llm_actors)} distributed LLM actors")
        return llm_actors

    # Test Ray distributed setup
    if torch.cuda.device_count() >= 2:
        ray_actors = setup_ray_cluster()

        # Distributed generation
        futures = []
        for i, actor in enumerate(ray_actors):
            future = actor.generate.remote(
                [f"Test prompt {i}"],
                SamplingParams(max_tokens=50)
            )
            futures.append(future)

        # Collect results
        results = ray.get(futures)

        print("Ray distributed results:")
        for i, result in enumerate(results):
            print(f"Actor {i}: {len(result[0].outputs[0].text)} chars generated")

    else:
        print("Ray distributed setup requires 2+ GPUs")

except ImportError:
    print("Ray not available - install with: pip install ray")

Kubernetes Multi-Node Deployment

# Kubernetes deployment for multi-node inference
def generate_k8s_manifests(num_nodes=2, gpus_per_node=2):
    """Generate Kubernetes manifests for distributed inference"""

    manifests = {}

    # Deployment manifest
    deployment = f"""
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-distributed
spec:
  replicas: {num_nodes}
  selector:
    matchLabels:
      app: vllm-distributed
  template:
    metadata:
      labels:
        app: vllm-distributed
    spec:
      containers:
      - name: vllm
        image: vllm/vllm-openai:latest
        ports:
        - containerPort: 8000
        env:
        - name: MODEL_NAME
          value: "microsoft/DialoGPT-large"
        - name: TENSOR_PARALLEL_SIZE
          value: "{gpus_per_node}"
        resources:
          limits:
            nvidia.com/gpu: {gpus_per_node}
          requests:
            memory: "32Gi"
            cpu: "8"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 60
"""

    # Service manifest
    service = """
apiVersion: v1
kind: Service
metadata:
  name: vllm-service
spec:
  selector:
    app: vllm-distributed
  ports:
  - port: 80
    targetPort: 8000
  type: LoadBalancer
"""

    # Ingress manifest
    ingress = """
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: vllm-ingress
spec:
  rules:
  - host: vllm.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: vllm-service
            port:
              number: 80
"""

    manifests['deployment'] = deployment
    manifests['service'] = service
    manifests['ingress'] = ingress

    return manifests

# Generate and display manifests
k8s_manifests = generate_k8s_manifests(num_nodes=2, gpus_per_node=2)

print("Kubernetes Deployment Manifests:")
print("=" * 40)

for name, manifest in k8s_manifests.items():
    print(f"\n{name.upper()}:")
    print(manifest)

Load Balancing and Request Routing

Intelligent Load Balancer

class DistributedLoadBalancer:
    def __init__(self, llm_instances):
        self.instances = llm_instances
        self.request_count = 0
        self.instance_stats = {i: {'requests': 0, 'latency': 0} for i in range(len(llm_instances))}

    async def route_request(self, prompt, sampling_params):
        """Route request to best available instance"""

        # Simple round-robin for demonstration
        instance_id = self.request_count % len(self.instances)
        instance = self.instances[instance_id]
        self.request_count += 1

        # Track request
        self.instance_stats[instance_id]['requests'] += 1

        import time
        start_time = time.time()

        # Route to instance
        result = instance.generate([prompt], sampling_params)[0]

        end_time = time.time()
        latency = end_time - start_time

        # Update stats
        self.instance_stats[instance_id]['latency'] = (
            (self.instance_stats[instance_id]['latency'] +
             latency) / 2  # Rolling average
        )

        return result.outputs[0].text, instance_id

    def get_load_stats(self):
        """Get load balancing statistics"""

        total_requests = sum(stats['requests'] for stats in self.instance_stats.values())
        avg_latency = sum(stats['latency'] for stats in self.instance_stats.values()) / len(self.instance_stats)

        return {
            'total_requests': total_requests,
            'instance_stats': self.instance_stats,
            'average_latency': avg_latency,
            'load_distribution': [stats['requests'] for stats in self.instance_stats.values()]
        }

# Test load balancer
if torch.cuda.device_count() >= 2:
    # Create multiple instances
    instances = []
    for i in range(min(2, torch.cuda.device_count())):
        llm_instance = LLM(
            model="microsoft/DialoGPT-medium",
            tensor_parallel_size=1
        )
        instances.append(llm_instance)

    load_balancer = DistributedLoadBalancer(instances)

    # Test routing
    test_prompts = [f"Test request {i}" for i in range(6)]

    import asyncio
    async def test_routing():
        for prompt in test_prompts:
            result, instance_id = await load_balancer.route_request(
                prompt,
                SamplingParams(max_tokens=30)
            )
            print(f"Request routed to instance {instance_id}: {result[:50]}...")

        # Get stats
        stats = load_balancer.get_load_stats()
        print(f"\nLoad Stats: {stats}")

    await test_routing()
else:
    print("Load balancer test requires 2+ GPUs")

Adaptive Request Routing

class AdaptiveRouter:
    def __init__(self, llm_instances):
        self.instances = llm_instances
        self.performance_history = {i: [] for i in range(len(llm_instances))}

    async def route_adaptive(self, prompt, sampling_params):
        """Route based on instance performance and current load"""

        # Calculate instance scores based on recent performance
        instance_scores = {}

        for instance_id, history in self.performance_history.items():
            if history:
                avg_latency = sum(h['latency'] for h in history) / len(history)
                success_rate = sum(h['success'] for h in history) / len(history)
                # Higher score for lower latency and higher success
                score = success_rate / (avg_latency + 0.1)  # Avoid division by zero
            else:
                score = 1.0  # Default score for new instances

            instance_scores[instance_id] = score

        # Select best instance
        best_instance_id = max(instance_scores.keys(), key=lambda x: instance_scores[x])
        instance = self.instances[best_instance_id]

        import time
        start_time = time.time()

        try:
            result = instance.generate([prompt], sampling_params)[0]
            success = True
            response = result.outputs[0].text
        except Exception as e:
            success = False
            response = f"Error: {e}"

        end_time = time.time()
        latency = end_time - start_time

        # Update performance history
        self.performance_history[best_instance_id].append({
            'latency': latency,
            'success': success,
            'timestamp': time.time()
        })

        # Keep only recent history (last 100 requests)
        if len(self.performance_history[best_instance_id]) > 100:
            self.performance_history[best_instance_id] = self.performance_history[best_instance_id][-100:]

        return response, best_instance_id, success

# Test adaptive routing
if torch.cuda.device_count() >= 2:
    instances = [LLM("microsoft/DialoGPT-medium") for _ in range(min(2, torch.cuda.device_count()))]
    adaptive_router = AdaptiveRouter(instances)

    async def test_adaptive_routing():
        test_prompts = ["Hello", "How are you?", "What's the weather?", "Tell me a joke"]

        for prompt in test_prompts:
            response, instance_id, success = await adaptive_router.route_adaptive(
                prompt,
                SamplingParams(max_tokens=20)
            )
            status = "✅" if success else "❌"
            print(f"{status} Instance {instance_id}: {response[:40]}...")

    await test_adaptive_routing()
else:
    print("Adaptive routing test requires 2+ GPUs")

Fault Tolerance and Recovery

Instance Health Monitoring

class HealthMonitor:
    def __init__(self, llm_instances, health_check_interval=30):
        self.instances = llm_instances
        self.health_status = {i: {'healthy': True, 'last_check': 0} for i in range(len(llm_instances))}
        self.health_check_interval = health_check_interval

    async def monitor_health(self):
        """Continuously monitor instance health"""

        while True:
            for instance_id, instance in enumerate(self.instances):
                current_time = time.time()
                last_check = self.health_status[instance_id]['last_check']

                if current_time - last_check > self.health_check_interval:
                    # Perform health check
                    is_healthy = await self._check_instance_health(instance)

                    self.health_status[instance_id]['healthy'] = is_healthy
                    self.health_status[instance_id]['last_check'] = current_time

                    if not is_healthy:
                        print(f"⚠️ Instance {instance_id} is unhealthy")
                    else:
                        print(f"✅ Instance {instance_id} is healthy")

            await asyncio.sleep(self.health_check_interval)

    async def _check_instance_health(self, instance):
        """Check if instance is responding"""

        try:
            # Simple health check - try a short generation
            result = instance.generate(
                ["test"],
                SamplingParams(max_tokens=5, timeout=10)
            )

            return len(result) > 0 and len(result[0].outputs[0].text) > 0

        except Exception as e:
            print(f"Health check failed: {e}")
            return False

    def get_healthy_instances(self):
        """Get list of healthy instance IDs"""

        return [i for i, status in self.health_status.items() if status['healthy']]

    def get_health_report(self):
        """Get comprehensive health report"""

        total_instances = len(self.instances)
        healthy_instances = sum(1 for status in self.health_status.values() if status['healthy'])

        return {
            'total_instances': total_instances,
            'healthy_instances': healthy_instances,
            'unhealthy_instances': total_instances - healthy_instances,
            'health_percentage': (healthy_instances / total_instances) * 100 if total_instances > 0 else 0,
            'instance_details': self.health_status
        }

# Test health monitoring
health_monitor = HealthMonitor([llm] * min(2, max(1, torch.cuda.device_count())))

# Start monitoring
monitor_task = asyncio.create_task(health_monitor.monitor_health())

# Let it run briefly
await asyncio.sleep(5)

# Get health report
report = health_monitor.get_health_report()
print("Health Report:")
print(f"Healthy instances: {report['healthy_instances']}/{report['total_instances']}")

# Stop monitoring
monitor_task.cancel()

Automatic Failover

class FailoverManager:
    def __init__(self, primary_instances, backup_instances=None):
        self.primary_instances = primary_instances
        self.backup_instances = backup_instances or []
        self.active_instances = primary_instances.copy()
        self.failed_instances = set()

    async def execute_with_failover(self, prompt, sampling_params, max_retries=2):
        """Execute with automatic failover"""

        for attempt in range(max_retries + 1):
            if not self.active_instances:
                raise Exception("No active instances available")

            # Select instance (round-robin)
            instance_id = attempt % len(self.active_instances)
            instance = self.active_instances[instance_id]

            try:
                result = instance.generate([prompt], sampling_params)[0]
                return result.outputs[0].text, instance_id, attempt

            except Exception as e:
                print(f"Instance {instance_id} failed (attempt {attempt + 1}): {e}")

                # Mark instance as failed
                self.failed_instances.add(instance_id)
                self.active_instances.remove(instance)

                # Try to activate backup
                if self.backup_instances and attempt < max_retries:
                    backup_id = len(self.primary_instances) + len(self.active_instances)
                    if backup_id < len(self.primary_instances) + len(self.backup_instances):
                        backup_instance = self.backup_instances[backup_id - len(self.primary_instances)]
                        self.active_instances.append(backup_instance)
                        print(f"Activated backup instance {backup_id}")

        raise Exception("All instances failed")

    def get_failover_stats(self):
        """Get failover statistics"""

        return {
            'total_instances': len(self.primary_instances) + len(self.backup_instances),
            'active_instances': len(self.active_instances),
            'failed_instances': len(self.failed_instances),
            'backup_instances': len(self.backup_instances)
        }

# Test failover
primary_llm = LLM("microsoft/DialoGPT-medium")
backup_llm = LLM("gpt2")  # Simulating backup with different model

failover_manager = FailoverManager(
    primary_instances=[primary_llm],
    backup_instances=[backup_llm]
)

# Test failover (simulate failure by using invalid instance)
try:
    result, instance_id, attempts = await failover_manager.execute_with_failover(
        "Test prompt",
        SamplingParams(max_tokens=20)
    )
    print(f"Success: Instance {instance_id}, Attempts: {attempts}")
    print(f"Result: {result[:50]}...")

except Exception as e:
    print(f"Final failure: {e}")

stats = failover_manager.get_failover_stats()
print(f"Failover stats: {stats}")

Production Deployment Patterns

High Availability Setup

def create_ha_deployment_manifest():
    """Create high availability deployment manifest"""

    ha_manifest = """
apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-ha-deployment
spec:
  replicas: 3  # Multiple replicas for HA
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxUnavailable: 1
      maxSurge: 1
  selector:
    matchLabels:
      app: vllm-ha
  template:
    metadata:
      labels:
        app: vllm-ha
    spec:
      affinity:
        podAntiAffinity:
          preferredDuringSchedulingIgnoredDuringExecution:
          - weight: 100
            podAffinityTerm:
              labelSelector:
                matchLabels:
                  app: vllm-ha
              topologyKey: kubernetes.io/hostname
      containers:
      - name: vllm
        image: vllm/vllm-openai:latest
        ports:
        - containerPort: 8000
        env:
        - name: MODEL_NAME
          value: "microsoft/DialoGPT-large"
        - name: TENSOR_PARALLEL_SIZE
          value: "2"
        resources:
          limits:
            nvidia.com/gpu: 2
          requests:
            memory: "16Gi"
            cpu: "4"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 60
          periodSeconds: 30
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
---
apiVersion: v1
kind: Service
metadata:
  name: vllm-ha-service
spec:
  selector:
    app: vllm-ha
  ports:
  - port: 80
    targetPort: 8000
  type: LoadBalancer
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: vllm-ha-ingress
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /
spec:
  rules:
  - host: vllm-ha.example.com
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: vllm-ha-service
            port:
              number: 80
"""

    return ha_manifest

# Generate HA manifest
ha_manifest = create_ha_deployment_manifest()
print("High Availability Deployment Manifest:")
print("=" * 50)
print(ha_manifest)

Monitoring and Observability

Distributed Metrics Collection

# Distributed monitoring setup
class DistributedMonitor:
    def __init__(self, instance_ids):
        self.instance_ids = instance_ids
        self.metrics = {i: {} for i in instance_ids}

    async def collect_distributed_metrics(self):
        """Collect metrics from all distributed instances"""

        # In a real setup, this would query each instance's metrics endpoint
        for instance_id in self.instance_ids:
            # Simulate metric collection
            self.metrics[instance_id] = {
                'requests_per_second': 10 + instance_id,
                'average_latency': 0.5 + (instance_id * 0.1),
                'memory_usage': 0.7 + (instance_id * 0.05),
                'gpu_utilization': 0.8 + (instance_id * 0.03),
                'queue_length': instance_id * 2,
                'error_rate': instance_id * 0.01
            }

        return self.metrics

    def generate_cluster_report(self):
        """Generate comprehensive cluster report"""

        if not self.metrics:
            return "No metrics available"

        total_instances = len(self.metrics)
        total_rps = sum(m.get('requests_per_second', 0) for m in self.metrics.values())
        avg_latency = sum(m.get('average_latency', 0) for m in self.metrics.values()) / total_instances
        avg_memory = sum(m.get('memory_usage', 0) for m in self.metrics.values()) / total_instances
        avg_gpu = sum(m.get('gpu_utilization', 0) for m in self.metrics.values()) / total_instances

        report = ".2f"".3f"".3f"".3f"f"""
Cluster Performance Report
==========================
Total Instances: {total_instances}
Total RPS: {total_rps:.1f}
Average Latency: {avg_latency:.3f}s
Average Memory Usage: {avg_memory:.1f}
Average GPU Utilization: {avg_gpu:.1f}

Instance Details:
"""

        for instance_id, metrics in self.metrics.items():
            report += f"""
Instance {instance_id}:
  RPS: {metrics.get('requests_per_second', 0):.1f}
  Latency: {metrics.get('average_latency', 0):.3f}s
  Memory: {metrics.get('memory_usage', 0):.1f}
  GPU: {metrics.get('gpu_utilization', 0):.1f}
  Queue: {metrics.get('queue_length', 0)}
  Errors: {metrics.get('error_rate', 0):.3f}
"""

        return report

# Test distributed monitoring
monitor = DistributedMonitor([0, 1, 2])  # Simulate 3 instances

# Collect metrics
await monitor.collect_distributed_metrics()

# Generate report
report = monitor.generate_cluster_report()
print(report)

Summary

In this chapter, we've covered distributed inference techniques:

• Tensor Parallelism: Model distribution across multiple GPUs
• Pipeline Parallelism: Layer distribution and micro-batching
• Multi-Node Setup: Kubernetes and Ray-based distributed deployments
• Load Balancing: Intelligent request routing and failover
• Fault Tolerance: Health monitoring and automatic recovery
• Production Deployment: High availability and monitoring

Distributed inference enables scaling vLLM to handle enterprise workloads with high throughput and reliability.

Key Takeaways

1. Tensor Parallelism: Distribute models across GPUs for larger models
2. Load Balancing: Route requests intelligently for optimal performance
3. Fault Tolerance: Monitor health and provide automatic failover
4. Scalability: Scale from single GPU to multi-node clusters
5. Monitoring: Comprehensive observability for distributed systems

Next, we'll explore production deployment - serving vLLM with FastAPI, Docker, and Kubernetes.

---

Ready for the next chapter? Chapter 7: Production Deployment

Generated for Awesome Code Docs

What Problem Does This Solve?

Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for print, self, instance_id so behavior stays predictable as complexity grows.

In practical terms, this chapter helps you avoid three common failures:

• coupling core logic too tightly to one implementation path
• missing the handoff boundaries between setup, execution, and validation
• shipping changes without clear rollback or observability strategy

After working through this chapter, you should be able to reason about Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes as an operating subsystem inside vLLM Tutorial: High-Performance LLM Inference, with explicit contracts for inputs, state transitions, and outputs.

Use the implementation notes around vllm, instance, result as your checklist when adapting these patterns to your own repository.

How it Works Under the Hood

Under the hood, Chapter 6: Distributed Inference - Scaling Across GPUs and Nodes usually follows a repeatable control path:

1. Context bootstrap: initialize runtime config and prerequisites for print.
2. Input normalization: shape incoming data so self receives stable contracts.
3. Core execution: run the main logic branch and propagate intermediate state through instance_id.
4. Policy and safety checks: enforce limits, auth scopes, and failure boundaries.
5. Output composition: return canonical result payloads for downstream consumers.
6. Operational telemetry: emit logs/metrics needed for debugging and performance tuning.

When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.

Source Walkthrough

Use the following upstream sources to verify implementation details while reading this chapter:

• View Repo Why it matters: authoritative reference on View Repo (github.com).
• Awesome Code Docs Why it matters: authoritative reference on Awesome Code Docs (github.com).

Suggested trace strategy:

• search upstream code for print and self to map concrete implementation paths
• compare docs claims against actual runtime/config code before reusing patterns in production

Chapter Connections

• Tutorial Index
• Previous Chapter: Chapter 5: Performance Optimization - Maximizing Throughput and Efficiency
• Next Chapter: Chapter 7: Production Deployment - Serving vLLM at Scale
• Main Catalog
• A-Z Tutorial Directory