Google Cloud launches C4N virtual machines for heavy I/O

Google Cloud has made its C4N network- and storage-optimised virtual machine instances generally available, extending its Compute Engine range for data-intensive workloads.

The new instance family targets applications that need high network throughput or heavy block storage use, including databases, network appliances, analytics and some artificial intelligence inference tasks. Built on Google Cloud's Titanium offload architecture, the machines shift network and storage processing to dedicated hardware.

C4N runs on 5th Gen Intel Xeon Scalable processors, code-named Emerald Rapids. The instances offer up to 400 Gbps of network bandwidth and up to 95 million packets per second, alongside block storage throughput of up to 25 GiB/s and as many as 1 million input/output operations per second when paired with Hyperdisk Extreme.

Google Cloud positions C4N as a way for customers to avoid over-provisioning compute resources just to meet input/output requirements. The family can scale bandwidth, packet processing and storage performance across instance sizes from 2 to 192 virtual CPUs, with memory configurations of up to 1.5 TB.

Performance focus

Google Cloud distinguishes C4N from its general-purpose C4 virtual machines by tuning the newer family for predictable performance under sustained network and storage demand. For smaller instance sizes, C4N delivers between 25 Gbps and 50 Gbps of network bandwidth on 2 to 16 vCPU shapes, which may suit customers running I/O-bound tasks without needing much more compute.

The machines also raise internet egress bandwidth to as much as 200 Gbps. Packet processing for internet egress can reach 48 million packets per second.

Storage is a central part of the launch. C4N supports the full Hyperdisk portfolio, including Balanced, Balanced High Availability, Extreme, Throughput and ML options. With Hyperdisk Balanced, the instances can reach up to 20 GiB/s of block storage throughput and nearly 640,000 IOPS.

Google Cloud also reported application-level gains in internal testing against C4 machines, including up to 1.5 times more Nginx web requests per second for typical request sizes and as much as 45% better MySQL query throughput when data is primarily stored on disk.

Customer tests

Several partners and customers outlined early use cases for the platform across telecommunications, analytics, storage and high-performance computing.

Ericsson linked the instances to mobile core workloads running in public cloud settings. "5G Core workloads are inherently network-heavy, demanding high-throughput packet processing and deterministic latency that standard public cloud instances often struggle to maintain at scale. By leveraging the Google Cloud C4N compute family, we've found the ideal engine for Ericsson On-Demand. The C4N's architectural focus on network-optimized compute allows our 5G Core-as-a-Service to reach unprecedented throughput levels - like our recent 1 Tbps milestone - while maintaining the carrier-grade reliability our customers expect. It's no longer just about cloud-native; with C4N, we are delivering network-native performance in a public cloud environment," said Eric Parsons, VP, Head of Ericsson On-Demand, Ericsson.

Teradata said the instances suit intensive production workloads that combine analytics, AI and large data sets. "Teradata's Autonomous Knowledge Platform unifies production-grade AI, analytics, and data into a single integrated system - providing the context, governance, and performance backbone autonomous AI demands at scale. Customers rely on Teradata to run mission-critical, highly I/O-intensive workloads where performance and cost control directly determine value. Google Cloud C4N instances are well suited for these demanding workloads, delivering strong price-performance and supporting more efficient, optimized deployments. By leveraging C4N on Google Cloud, Teradata Cloud can help customers accelerate from insight to action - scaling enterprise intelligence with confidence and driving greater impact from their data and AI investments," said Kevin Dougherty, Senior Director of Product Management, Core Platform, Teradata.

NetApp tied the launch to storage-heavy AI deployments that need more bandwidth between compute and data services. "With the next-generation network and storage bandwidth of C4N VMs, Google Cloud NetApp Volumes will unlock new levels of performance to support our customers' most demanding AI workloads. By collaborating to extend Google Cloud NetApp Volumes support for the C4N VM family, Google and NetApp are deepening our partnership to address real customer challenges. Together, we're delivering data-in-place AI and analytics solutions that simplify architectures, maximize performance, and turn data into impact," said Pravjit Tiwana, Senior Vice President and General Manager of Cloud Storage and Services, NetApp.

Sycomp provided one of the more detailed performance accounts, testing storage throughput on the instances. "Most Compute Engine instances ship with a single high-speed network interface. The new C4N doubles the bandwidth potential with two 200 GbE interfaces. That architectural shift is significant. It means we can dedicate both networks entirely to storage traffic, doubling the available bandwidth for data-intensive workloads, and achieving 2x storage performance over the previous generation. The C4N was announced just weeks ago and is already active in Sycomp's test environment, ensuring our customers can evaluate the latest GCP capabilities without delay. Google Cloud's published maximum hyperdisk balanced performance for the C4N is 20 GiB/s. In our tests, with three storage servers Sycomp achieved 58.5 GiB/s on read and 58.6 GiB/s on write, with ten C4N storage servers we achieved 195 GiB/s read and write - 97% of the theoretical ceiling with zero platform-specific tuning. That's a strong starting point, and there's measurable room to close the remaining gap through configuration work we can finetune. The C4N isn't just faster - it changes the price-performance equation for storage workloads on Google Cloud," said Scott Fadden, Senior HPC Solutions Architect, Sycomp.

Intel also commented on the processor and infrastructure design behind the product. "Google Cloud's introduction of C4N highlights how infrastructure innovation and a strong silicon foundation can help customers address increasingly data-intensive workloads. With Intel Xeon and Custom Infrastructure Processing Unit (IPU), C4N delivers the performance and efficiency needed for demanding network optimized environments," said Srini Krishna, Intel Fellow, Data Centre Products, Intel.