Deep Dive: AWS Graviton4 Chip Internals – How It Cuts Container Workload Costs by 30%

AWS’s Graviton line of custom ARM-based processors has disrupted cloud compute since the first generation launched in 2018. The latest iteration, Graviton4, promises up to 30% lower costs for containerized workloads compared to x86-based equivalents. This deep dive breaks down the chip’s internal architecture, and explains exactly how its design choices deliver these savings for Kubernetes, ECS, and serverless container workloads.

Graviton4 Architecture: Core Internals

Graviton4 is built on TSMC’s 4nm process node, a step up from Graviton3’s 5nm design, enabling higher transistor density and better power efficiency. At its heart are 96 custom ARM Neoverse V2 cores, 50% more than Graviton3’s 64-core count, with support for ARM’s Scalable Vector Extensions 2 (SVE2) for accelerated media, compression, and data processing workloads common in containers.

Cache and Memory Subsystem

Each Neoverse V2 core includes 2MB of private L2 cache, for a total of 192MB L2 across the chip. A shared 96MB L3 cache reduces cross-core communication latency for multi-container workloads. The memory controller supports 12 channels of DDR5-6400 RAM, delivering 614 GB/s of peak bandwidth, 75% more than Graviton3, critical for memory-intensive containerized applications like Redis, PostgreSQL, and data analytics pipelines.

Networking and Nitro Integration

Graviton4 is tightly integrated with AWS’s Nitro System, offloading networking, storage, and security processing to dedicated Nitro cards. This eliminates hypervisor overhead for container workloads, freeing up more core cycles for application logic. PCIe 5.0 support enables 2x faster connectivity to Nitro cards and attached storage, reducing I/O latency for stateful containers.

Why Container Workloads See 30% Cost Reductions

Containerized workloads are uniquely suited to Graviton4’s design, driving the 30% cost savings claim from three key factors:

• Performance per Watt Leadership: Graviton4 delivers 30% better compute performance per watt than Graviton3, and 2x better than comparable x86 instances. Lower power consumption reduces AWS’s infrastructure costs, passed to customers via 20% lower on-demand pricing for c8g (Graviton4 compute-optimized) instances vs c7i (x86 compute-optimized) equivalents.
• Higher Pod/Container Density: The 50% core count increase and larger cache enable 40% more container pods per instance for Kubernetes workloads, reducing the total number of instances needed to run a given workload.
• Workload-Optimized Acceleration: SVE2 support accelerates common containerized tasks including image resizing, log compression, and TLS termination, reducing the number of cores needed to deliver target throughput by up to 25% for media and edge container workloads.

Benchmarking Container Workload Performance

AWS and third-party benchmarks validate the 30% cost reduction claim for container workloads:

• Nginx Web Server Containers: A c8g.24xlarge instance (96 cores) delivers 1.3x the requests per second of a c7i.24xlarge (96 Intel Sapphire Rapids cores) at 20% lower hourly cost, resulting in 30% lower cost per 1M requests.
• Redis In-Memory Cache: Graviton4 instances deliver 28% higher throughput per dollar for Redis clusters running in ECS, due to higher memory bandwidth and lower latency.
• Kubernetes Pod Density: EKS clusters running on Graviton4 support 40% more nginx-alpine pods per node than x86 nodes, reducing cluster management overhead and total instance count by 30% for scale-out workloads.

Migrating Container Workloads to Graviton4

Most container workloads require no code changes to run on Graviton4, as long as container images are multi-architecture (supports both x86 and ARM64). AWS provides tooling to simplify migration:

• Build multi-arch images with Docker Buildx or AWS CodeBuild, which automatically compiles for ARM64 and x86.
• EKS and ECS support mixed clusters with both Graviton and x86 nodes, enabling gradual migration with no downtime.
• AWS App2Container automatically containers and migrates legacy applications to Graviton-compatible container images.

Conclusion

Graviton4’s 4nm process, Neoverse V2 cores, and Nitro integration combine to deliver 30% lower costs for container workloads, without sacrificing performance. For teams running large-scale Kubernetes, ECS, or serverless container workloads, the chip’s internals remove long-standing x86 tax, making ARM-based compute the default choice for cloud-native workloads.