Introducing Z4M: storage-optimized compute for AI/ML and parallel file systems

Google Cloud made its storage-optimized Z4M machine series generally available in Google Compute Engine as both VM and bare-metal instances, targeting AI/ML training and inference workloads such as checkpointing, distributed KV cache and parallel file systems. Powered by fifth-generation Intel Xeon Scalable processors and Titanium, Z4M offers up to 168,000 GiB of Local SSD, up to 192 vCPUs, up to 1,536 GiB of memory and up to 400 Gbps of RDMA networking, which Google says cuts data-access latency by up to 60% versus TCP. Google reports up to 15.6M random-read IOPS and 75,600 MiB/s sequential-read throughput, improving on Z3 by up to 70% and 110% respectively, with a 1.4:1 balanced LSSD-to-networking throughput ratio.

Today, we’re excited to announce the general availability of our next-generation Storage-optimized Z4M machine https://docs.cloud.google.com/compute/docs/storage-optimized-machines series in Google Compute Engine, available as both Virtual Machine VM and bare-metal instances. We built Z4M to provide storage for AI/ML training and inference workloads such as checkpointing, distributed key-value KV cache and parallel file systems — workloads that handle huge data volumes and require massive parallel data processing, as well as high storage and networking throughput. Low latency access to data is equally critical to keep GPUs and TPUs fully utilized. Powered by the fifth-generation Intel Xeon Scalable processors and paired with the latest enhancements in Titanium https://cloud.google.com/titanium?e=48754805 , Z4M provides up to 168,000 GiB of Local SSD LSSD storage coupled with up to 400 Gbps of Cloud Remote Direct Memory Access RDMA networking. RDMA improves the performance of these demanding workloads by reducing data-access latency by up to 60% compared to TCP networking, so you can increase your applications’ throughput while right-sizing your cloud investment. The Z4M VM portfolio lets you scale your infrastructure and clusters to meet workload requirements by providing a large total LSSD capacity as well as 875 GiB of LSSD capacity per vCPU. Further, Z4M bare-metal instances give you direct access to the physical hardware without a virtualization layer, reducing latency for workloads such as custom hypervisors and workloads with specific licensing needs. Z4M bare-metal instances are also ideal for agentic AI workloads, enabling developers to run isolated sandboxes at scale, including with Agent Substrate. In addition to 168,000 GiB of LSSD storage, Z4M VMs and bare metal instances offer up to 192 vCPUs and up to 1,536 GiB of memory for at-scale data processing. Z4M instances are based on Titanium SSDs https://cloud.google.com/compute/docs/disks/local-ssd , which offload local storage processing from CPU resources to deliver real-time data processing, low-latency, high-throughput storage performance and enhanced storage security. Z4M delivers up to 15.6M random-read IOPS and up to 75,600 MiB/s sequential-read throughput, improving the LSSD storage performance and throughput compared to Z3 by up to 70% and 110% respectively. Z4M provides low-latency access to the storage layer using 400 Gbps of RDMA https://docs.cloud.google.com/vpc/docs/rdma-network-profiles -enabled standard networking.This helps keep GPUs and TPUs fully utilized even when processing large multimodal datasets, by transferring massive amounts of data at line rate using NVIDIA GPUDirect Storage https://cloud.google.com/blog/topics/ai-infrastructure/google-cloud-infrastructure-at-nvidia-gtc-berlin-2026?e=48754805 and TPUDirect Storage https://cloud.google.com/blog/products/compute/tpu-8t-and-tpu-8i-technical-deep-dive?e=48754805 . At the same time, Z4M transfers data at full line rate while processing data locally, with a balanced-LSSD-to-networking-throughput ratio of 1.4:1. | Workload Category | Typical Applications | Z4M Benefits | |---|---|---| | AIML training and inference | Checkpointing, distributed Key-Value cache | Large LSSD capacity paired with 400 Gbps low-latency RDMA network that supports GPUDirect Storage and TPUDirect Storage, keeping GPUs and TPUs fully utilized even when processing large multimodal datasets. | | Parallel file systems and Enterprise-class storage | Weka, Sycomp, Silk, VDURA | Very high vCPU:LSSD ratio of 1:875 combined with a balanced LSSD:networking throughput of 1.4: to improve applications’ performance by processing data locally while transferring data to clients at full line rate. | | Business-critical enterprise Databases | SQL, NoSQL, Vector DBs, data search | Sustained LSSD high I/O performance and throughput to sustain a large number of transactions per second. | "Nutanix is committed to helping organizations run their applications and data anywhere. Bringing Nutanix Cloud Clusters NC2 to Google Cloud's new Z4M metal instances reflects that mission and our continued partnership with Google Cloud. By combining Z4M’s exceptional I/O performance and high storage density along with Intel’s advanced compute and AMX-driven efficiency, NC2 on Z4M provides the scale, density, and performance required to power everything from dense VDI clusters to demanding, data-intensive AI workloads." - Saveen Pakala, Vice President of Product Management, Nutanix "Google's Z4M VMs bring Cloud RDMA to a storage-optimized VM with much higher local SSD density than previous generations. In our testing, Cloud RDMA on Z4M demonstrated outstanding performance with up to 3.7x lower latency and up to 6x lower CPU usage than TCP. For our customers, that means faster, more predictable performance across a wide variety of use cases, from I/O-intensive databases to analytics and AI workloads, with the storage density to scale capacity efficiently." - Adik Sokolovski, Chief R&D Officer, Silk “We're excited to support Google Cloud's new Z4M platform as part of the Sycomp Intelligent Data Storage Platform. With greater capacity per node and the continued evolution of the Z-series architecture, Z4M helps customers achieve new levels of performance and efficiency. Supporting innovations like Z4M reinforces Sycomp's commitment to delivering high-performance storage solutions across the Google Cloud ecosystem.” - Scott Fadden, Senior HPC Solutions Architect, Sycomp "We've worked alongside Google Cloud for years on the most demanding AI and machine learning workloads. In our testing, we found the new Z4M machines deliver roughly twice the read throughput and up to 79% more IOPS, generation over generation Z3 family . This means more data, more tokens, and more finished work. We’re excited to see what our joint customers build with these powerful machines." - Ajay Singh, Chief Product Officer, WEKA You can easily configure, deploy and manage large Z4M clusters using Google Cloud’s Cluster Director https://docs.cloud.google.com/ai-hypercomputer/docs/cluster-capabilities , which offers advanced placement and maintenance capabilities alongside the Cluster Toolkit https://docs.cloud.google.com/cluster-toolkit/docs/overview for rapid cluster deployment. You can view and deploy your workloads in a compact topology to maximize performance, or spread topology for high availability by targeting availability domains. You can combine or stagger the maintenance across these availability domains. Additionally, you can co-locate Z4M instances with your GPU and TPU clusters to process very large datasets in real time and at scale. Both Z4M VMs and bare-metal instances make it easier for you to plan ahead and schedule maintenance operations by providing notice from the system several days in advance of required maintenance. For Z4M VMs with 42,000 GiB or less of LSSD storage, you can live-migrate an instance during maintenance events. Z4M VMs with 84,000 GiB or more of LSSD and Z4M bare metal instances must be terminated and restarted while preserving your data through planned maintenance events. Z4M VMs and bare-metal instances support Hyperdisk https://cloud.google.com/compute/docs/disks/hyperdisks , Google Cloud’s workload-optimized block storage, which lets you optimize the performance for each workload by independently tuning the storage performance and capacity for each instance. Specifically, they are compatible with Hyperdisk Balanced https://cloud.google.com/compute/docs/disks/hd-types/hyperdisk-balanced , Hyperdisk Throughput https://cloud.google.com/compute/docs/disks/hd-types/hyperdisk-throughput , and Extreme https://cloud.google.com/compute/docs/disks/hd-types/hyperdisk-extreme Hyperdisk https://cloud.google.com/compute/docs/disks/hyperdisks storage for scalable, high-performance network-attached storage, supporting up to 512 TiB of capacity per instance. For general-purpose workloads, Hyperdisk Balanced, with up to 320K IOPS per instance, offers a mix of performance and cost-efficiency. Hyperdisk Extreme delivers ultra-low latency and supports up to 500K IOPS and 12,500 MiB/s throughput per Z4M VM and bare-metal instance, making it well-suited for demanding database workloads. Z4M VMs and bare metal instances are available today in select regions worldwide. To start using Z4M instances, choose Z4M under the Storage-Optimized machine family when creating a new VM or GKE node pool in the Google Cloud console. Learn more at the Z4M machine series https://docs.cloud.google.com/compute/docs/storage-optimized-machines page. Contact your Google Cloud sales https://cloud.google.com/contact?e=48754805&hl=en representative for more information on regional availability.