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

Today, we’re excited to announce the general availability of our next-generation Storage-optimized Z4M machine 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, 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.  Z4M VMs and bare-metal instances 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, 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.  Cloud RDMA and direct storage access  Z4M provides low-latency access to the storage layer using 400 Gbps of RDMA-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 and TPUDirect Storage. 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.  Built for demanding workloads 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. What customers and partners are saying "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 a

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