GCP – N4D now GA: Gain up to 3.5x price-performance for scale-out workloads

In today’s competitive environment, IT leaders are faced with supporting application scale, rolling out more features, and enabling high-bar customer experiences. This creates a direct and complex challenge: finding the right balance between performance and total cost of ownership (TCO) for the general-purpose workloads that power everyday business operations.

Today, we are announcing the general availability of the N4D machine series, the latest addition to Google Compute Engine’s cost-optimized, general-purpose portfolio. Addressing a wide range of workloads, such as web and application servers, data analytics platforms, and containerized microservices, N4D provides a flexible and price-performant solution.

The N4D machine series combines Google’s Titanium infrastructure with 5th Gen AMD EPYC™ “Turin” processors, delivering up to 3.5x the throughput for web-serving workloads vs. the previous-generation N2D.  N4D offers predefined shapes of up to 96 vCPUs and 768 GB of DDR5 memory, up to 50 Gbps of networking bandwidth, and Hyperdisk Balanced and Throughput storage. To deliver a blended cost savings, N4D allows you to move beyond rigid instance sizing for both compute and storage, with Custom Machine Types to independently configure the exact number of vCPUs and amount of memory, complemented with Hyperdisk, for tuning disk storage performance and capacity. For the most demanding general purpose workloads, pair N4D together with consistently high performance  of C4D.

Google Cloud provides workload-optimized infrastructure to ensure the right resources are available for every task. Titanium in particular, with its multi-tier offloads and security capabilities, is foundational to that infrastructure. Titanium offloads networking and storage processing to free up the CPU, and its dedicated SmartNIC manages all I/O, ensuring the AMD EPYC cores are reserved exclusively for your application. Titanium is part of Google Cloud’s vertically integrated stack — from the custom silicon in our servers to our planet-scale network traversing 7.75 million kilometers of terrestrial and subsea fiber across 42 regions — that is engineered to maximize efficiency and provide the ultra-low latency and high bandwidth to customers at global scale.

A new standard for price-performance

N4D machine series doesn’t just inch past the previous N2D generation; it sprints, delivering up to 50% higher price-performance for general computing workloads and up to 70% better price-performance for Java workloads. For web-serving workloads, N4D leverages Titanium and AMD’s Turin processors to drive incredible throughput. This results in up to 3.5x the price-performance vs N2D, driving faster response times and a better overall experience for your end-users.

Complementing C4D machine series

Earlier this year, we introduced our general-purpose C4D machine series built on the same underlying processor as N4D. Its consistently high performance and enterprise features like advanced maintenance support, larger shapes, and our next-gen Titanium Local SSDs, make C4D a great fit for critical workloads. In fact, customers such as Silk and Chess.com report greater than 40% improvement in performance with C4D over prior generations. 

But critical applications are only part of the story. A modern cloud architecture must also run countless general-purpose workloads where flexibility and price-performance are key. That’s why we designed N4D — as a complement to C4D. By leveraging C4D and N4D in tandem, you unlock the full spectrum of enterprise features, performance, flexibility, and cost-optimization, choosing:

• C4D for consistent performance: This is your solution for the most demanding, latency-sensitive applications. With up to 200 Gbps networking, Local SSD support along with larger shapes up to 384 vCPUs and bare metal options, C4D delivers predictable, high-end performance for large databases, high-traffic ad and game servers, and demanding AI/ML inference workloads.

• N4D for flexible cost-optimization: This is the engine for the vast majority of your general-purpose workloads. N4D’s leading price-performance, low cost, and flexibility allow you to slash TCO for applications like web servers, microservices, and development environments.

This approach is already delivering real-world results, allowing customers like Verve to optimize their business from both ends.

The Custom Machine Type and Hyperdisk advantage

Custom Machine Types are a key differentiator for Google Cloud, letting you go beyond predefined “T-shirt sizes”. Instead of forcing your workload into a box, you can tailor the infrastructure to fit your workload’s needs, saving on cost. For instance, a memory-intensive workload requiring 16 vCPUs and 70 GB of RAM might typically be placed on a predefined N4D-highmem-16 shape, forcing you to pay for unused resources. With CMTs, you provision the exact 16 vCPU and 70 GB configuration, eliminating that waste and achieving up to 17% cost savings. 

With shapes of up to 96 vCPUs and 768 GB of DDR5 memory, the combination of Custom Machine Types and N4D lets you dial in the exact resources you need with flexible vCPU-to-memory ratios along with extended memory support.

This granular control and TCO advantage extends beyond compute to your storage. Just as Custom Machine Types let you break free from fixed vCPU-to-memory ratios, Hyperdisk unbundles storage performance from capacity, letting you independently tune capacity and performance to precisely match your workload’s block storage requirements.

This is further enhanced by Hyperdisk Storage Pools for Hyperdisk Balanced volumes, which let you provision performance and capacity in aggregate, rather than managing each volume individually. The result is simpler management, higher efficiency, an easier path for modernizing SAN workloads — all this while helping you lower your storage TCO by as much as 30-50%.

Get started with N4D today

Adopting the latest N4D VM series is easy, particularly if you use Google Kubernetes Engine (GKE), where our custom compute classes remove the operational hurdles of migrating workloads to new hardware. Just add N4D to your prioritized list of VM types to ensure your workloads have the performance and flexibility they need to scale.

N4D is now available in us-central1 (Iowa), us-east1 (South Carolina), us-west1 (Oregon), us-west4 (Las Vegas), europe-west1 (Belgium), and europe-west4 (Netherlands). 

Check for the latest availability on our Regions and Zones page and deploy your first instance today in the Google Cloud console or with GKE. Learn more about N4D details here in documentation.

^{1. 9xx5C-044 – Testing by AMD Performance Labs as of 10/21/2025. N4D-standard-16 score comparison to N2D-standard-16 running FFmpeg v6.1.1 benchmark (average of 2x encode and 2x transcode) on Ubuntu24.04LTS OS with 6.8.0-1021-gcp kernel, SMT On.}

^{Performance uplift (normalized to N2D):}

^{Ffmpeg_raw_vp9                   1.76
Ffmpeg_h264_vp9                1.76
Ffmpeg_raw_h264                1.71
Ffmpeg_vp9_h264                1.76
FFmpeg average                   1.75}

^{Cloud performance results presented are based on the test date in the configuration. Results may vary due to changes to the underlying configuration, and other conditions such as the placement of the VM and its resources, optimizations by the cloud service provider, accessed cloud regions, co-tenants, and the types of other workloads exercised at the same time on the system}