AWS's Graviton4 is the latest generation of their custom ARM-based processors. Building on the success of Graviton3, these new processors deliver notable improvements in performance, efficiency, and cost-effectiveness.

> Key Takeaways > > - Graviton4 delivers 30%+ faster compute performance and 75% more memory bandwidth than Graviton3 > - Organizations can expect 20-40% cost savings compared to equivalent x86 instances > - Most modern languages and runtimes (Node.js, Python, Java, Go, Rust) support ARM natively > - Multi-architecture container builds simplify migration with minimal application changes

What is AWS Graviton?

Graviton is AWS's family of custom ARM-based processors designed specifically for cloud workloads. Unlike traditional x86 processors from Intel and AMD, Graviton chips use the ARM architecture, offering:

• Better price-performance ratio
• Lower power consumption
• Optimized for cloud-native workloads
• Strong ecosystem support

What Performance Improvements Does Graviton4 Deliver Over Graviton3?

Performance Gains

Graviton4 delivers substantial improvements over Graviton3:

• 30%+ faster compute performance
• 50% more cores per instance
• 75% more memory bandwidth
• Enhanced floating-point performance

Enhanced Features

• Improved branch prediction for better single-thread performance
• Larger caches for reduced memory latency
• DDR5 memory support for higher bandwidth
• PCIe Gen5 for faster I/O operations

Available Instance Types

General Purpose (M8g/M8gd)

Best for diverse workloads:

• Web applications
• Application servers
• Development environments
• Small to medium databases

Compute Optimized (C8g/C8gd)

Ideal for compute-intensive tasks:

• Batch processing
• Scientific modeling
• Gaming servers
• Video encoding

Memory Optimized (R8g/R8gd)

Optimized for memory-intensive applications:

• In-memory databases
• Real-time analytics
• High-performance caching

Storage Optimized (I8g)

Designed for high I/O workloads:

• Data warehousing
• Distributed file systems
• Log processing

How Do You Migrate Workloads From x86 to Graviton4?

Assessing Workload Compatibility

Before migrating, evaluate your applications:

Step-by-Step Migration

Start with development/test environments:

# Launch a Graviton instance for testing
Use the latest Amazon Linux ARM64 AMI for your region
aws ec2 run-instances \
  --instance-type t4g.medium \
  --image-id <your-arm64-ami-id> \
  --key-name your-key-pair

Build multi-architecture container images:

# Dockerfile
FROM --platform=$TARGETPLATFORM node:20-alpine
WORKDIR /app
COPY package.json ./
RUN npm ci --only=production
COPY . .

CMD ["node", "server.js"]

docker buildx build \
  --platform linux/amd64,linux/arm64 \
  -t your-app:latest \
  --push .

# Terraform example
resource "aws_instance" "app_server" {
  ami           = "ami-xxxxxxxxxxxxxxxxx" # Use the latest Amazon Linux ARM64 AMI for your region
  instance_type = "m8g.xlarge"  # Graviton4
tags = {
    Name = "app-server-graviton"
  }
}

# Kubernetes deployment with mixed architecture
apiVersion: apps/v1
kind: Deployment
metadata:
  name: my-app
spec:
  replicas: 10
  template:
    spec:
      affinity:
        nodeAffinity:
          preferredDuringSchedulingIgnoredDuringExecution:
          - weight: 70
            preference:
              matchExpressions:
              - key: kubernetes.io/arch
                operator: In
                values:
                - arm64
          - weight: 30
            preference:
              matchExpressions:
              - key: kubernetes.io/arch
                operator: In
                values:
                - amd64

Performance Benchmarks

Web Application Performance

Testing with a Node.js application:

• Graviton4: 45,000 requests/second
• x86 (Comparable): 35,000 requests/second
• Improvement: 28%

Database Queries

PostgreSQL benchmark:

• Graviton4: 125,000 TPS
• x86 (Comparable): 95,000 TPS
• Improvement: 32%

Container Workloads

Kubernetes cluster performance:

• Graviton4: 40% more pods per node
• Memory efficiency: 15% better utilization

Best Practices

Application Optimization

Monitoring and Observability

• Use CloudWatch for performance metrics
• Monitor memory bandwidth utilization
• Track cost savings in Cost Explorer
• Set up alerts for anomalies

Security Considerations

• Enable Nitro Enclaves for sensitive workloads
• Use IMDSv2 for instance metadata
• Apply security patches regularly
• Implement proper IAM roles

Common Migration Challenges

Binary Dependencies

Some applications ship x86-only binaries:

• Check for ARM builds or alternatives
• Use emulation as temporary workaround (performance penalty)
• Contribute ARM builds to open source projects

Performance Profiling

Different performance characteristics may require tuning:

• Memory access patterns differ from x86
• Cache behavior may vary
• Re-profile and optimize for ARM

CI/CD Pipeline Updates

Build pipelines need multi-architecture support:

• Add ARM build targets
• Use cross-compilation or native ARM builders
• Test on both architectures

Future Outlook

AWS continues investing in Graviton:

• Expect regular generational improvements
• Broader instance type availability
• Enhanced software ecosystem
• Better tooling and migration support

How BeyondScale Can Help

At BeyondScale, we specialize in cloud infrastructure implementation and migration. Whether you're planning a migration from x86 to Graviton4 or optimizing your existing ARM workloads for maximum cost savings, our team can help you execute a seamless transition with minimal risk.

Explore our Implementation Services to learn more.

Conclusion

AWS Graviton4 processors bring real improvements in cloud computing efficiency. With better performance and lower costs, migrating to Graviton makes sense for most workloads.

The key to successful migration is careful planning, thorough testing, and gradual rollout. Start with non-production workloads, validate performance, and progressively move production workloads to realize the full benefits of Graviton4.

Frequently Asked Questions

How does Graviton4 performance compare to x86 processors?

AWS Graviton4 delivers over 30% faster compute performance compared to Graviton3 and typically outperforms comparable x86 instances by 20-30% in web application and database workloads. In PostgreSQL benchmarks, Graviton4 achieved 32% higher transactions per second, while costing 20-40% less than equivalent x86 instances.

Are ARM-based Graviton instances compatible with my existing applications?

Most modern languages and runtimes including Node.js, Python, Java, Go, Rust, and .NET Core have full ARM support. The primary compatibility challenges come from legacy applications or third-party tools that ship x86-only binaries. It is recommended to audit your dependency chain and test on Graviton instances before committing to a full migration.

How much effort does it take to migrate from x86 to Graviton4?

Migration effort varies by workload complexity. Cloud-native containerized applications can often migrate by rebuilding container images for ARM64 using multi-architecture Docker builds. Legacy applications with x86-only dependencies require more work, including finding ARM-compatible alternatives or using emulation as a temporary bridge.

What cost savings can I expect from switching to Graviton4 instances?

Organizations typically see 20-40% cost savings when switching from equivalent x86 instances to Graviton4. Web server workloads commonly save around 30%, while batch processing workloads can see savings up to 40%. These savings come from both lower per-instance pricing and improved performance per core.