Your guide to Azure Compute at Microsoft Ignite 2025
The countdown to Microsoft Ignite 2025 is almost over— Microsoft Ignite - November 18–21, 2025! Whether you’ll be joining us in person or tuning in virtually, this guide is your essential resource for everything Azure Compute. Explore the latest advancements, connect with product experts, and expand your cloud skills through curated sessions and interactive experiences. Attendees will have the opportunity to dive deep into new product capabilities and solutions, including ways to boost virtual machine performance, enhance resiliency, and optimize cloud operations. Be sure to add these sessions to your schedule for a personalized and can’t-miss Ignite experience. Bookmark this guide for quick access to all the latest Azure Compute news and updates throughout Ignite 2025! Featured sessions Tuesday BRK171: What's new and what's next in Azure IaaS Level: Intermediate 200 In this session, we’ll introduce the latest capabilities across compute, storage, and networking. Uncover the advancements in Azure IaaS, driving performance, resiliency, and cost efficiency. We will present how Azure’s global backbone, enhanced capabilities, and expanding portfolio can support mission-critical, cloud native and AI workloads —while built-in security and flexible tiering help right-size app deployments and accelerate modernization. Tuesday, November 18 | 2:30 PM-3:15 PM PST Wednesday BRK430: Inside Azure Innovations with Mark Russinovich Level: Advanced 300 Join Mark Russinovich, CTO and Technical Fellow of Microsoft Azure. Mark will take you on a tour of the latest innovations in Azure architecture and explain how Azure enables intelligent, modern, and innovative applications at scale in the cloud, on-premises, and on the edge. Featuring some of the latest Compute announcements with Azure Boost. Wednesday, November 19, 2:45 PM PST Other related IaaS sessions Use the following as a guide to build your session schedule with an emphasis on our Azure Compute topics. These sessions will be in person and recorded. Sessions Tuesday-Thursday will be live streamed. Thursday BRK176: Driving efficiency and cost optimization for Azure IaaS deployments Level: Intermediate 200 Control cloud spend without compromising performance. This session shows how Azure IaaS helps IT leaders optimize costs through flexible pricing, built-in tools, and smart resource planning. Learn how to align infrastructure choices with workload requirements, reduce TCO, and make informed decisions that support growth and innovation. You will gain a deeper understanding of how Azure delivers a comprehensive set of services, tools, and financial instruments to optimize your cloud costs at scale. Thursday, November 20 th , 9:45 AM PST BRK217: Resilience by design: Secure, scalable, AI-ready cloud with Azure Level: Advanced 300 Resiliency is foundational. Explore how resiliency on Azure enables secure, scalable, AI-ready cloud architectures. Learn to set resilience goals, simulate failures, and orchestrate recovery. See live demos and discover how shared responsibility empowers teams to deliver trusted, resilient outcomes. Thursday, November 20 th , 1:00 PM PST BRK178: Architecting for resiliency on Azure Infrastructure Level: Intermediate 200 Discover how to build resilient cloud solutions on Azure by leveraging availability zones, multi-region deployments, and fungible products. This session explores architectural patterns, platform capabilities, and best practices to ensure high availability, fault tolerance, and business continuity for mission-critical workloads in dynamic and distributed environments. Thursday, November 20, 1:00 PM PST BRK148: Architect resilient apps with Azure backup and reliability features Level: Advanced 300 Learn to use self-serve tools to strengthen zonal resiliency for critical workloads. Assess and validate resilience across VMs, DBs, and containers. Explore enhanced data and cyber resiliency with immutability and threat detection to guard against ransomware. Discover expanded workload coverage and real-time insights to proactively protect your applications and infrastructure. Thursday, November 20, 3:30 PM PST Friday BRK146: Resiliency and recovery with Azure Backup and Site Recovery Level: Advanced 300 This session will show how to secure, detect threats, and quickly recover critical workloads across Azure environments using advanced backup and disaster recovery solutions. It covers modern techniques like threat-aware backups, container protection, and seamless disaster recovery to help meet compliance and recovery objectives. Friday, November 21, 9:00 AM PST BRK149: Unlock cloud-scale observability and optimization with Azure Level: Advanced 300 In this session, we'll deep dive into how Azure Monitor delivers end-to-end observability across your cloud and hybrid environments, helping you detect issues early and reduce mean time to recovery. We'll also share how new Copilot in Azure agents can extend this visibility into actionable cost and carbon efficiency insights—helping you identify optimization opportunities, validating recommendations, and streamlining resource performance for business impact. Friday, November 21 st , 10:15 AM PST BRK173: Azure IaaS best practices to enhance performance and scale Level: Advanced 300 Azure IaaS can deliver excellent performance and scalability across a broad range of workloads. With high-throughput storage, low-latency networking, and intelligent auto-scaling, Azure supports demanding apps with precision. Learn how to optimize compute, storage, and network resources to meet performance goals, reduce costs, and scale confidently across global regions. Dive into the latest capabilities Azure Boost, Compute Fleet, Azure Virtual Machines, Azure Storage and Networking offer. Friday, November 21, 10:15 AM PST BRK172: Powering modern cloud workloads with Azure Compute Level: Advanced 300 Uncover new VM offerings announcements and explore innovations like Azure Boost. Dive into the latest compute innovation at the core of Azure IaaS. Whether you're running mission-critical enterprise apps or scaling cloud-native services, discover how these innovations are unlocking new value for customers and get a preview of what’s coming next. Friday, November 21, 11:30 AM PST BRK168: Azure IaaS platform security deep dive Level: Advanced 300 As organizations accelerate their cloud adoption, robust security for your Infrastructure as a Service platform is more critical than ever. This session will provide a comprehensive exploration of Azure’s security architecture, best practices, and innovations across four pillars: foundational security, compute security, network security, and storage security. Attendees will gain actionable insights to strengthen their cloud posture, ensure compliance, and protect sensitive workloads. Friday, November 21 st ,11:30 AM PST Upskill yourself with hands on labs This section explains that live demos and hands-on labs are exclusively available to those who attend in person, providing them with a direct, firsthand experience. Tuesday LAB500: Attain unified observability and optimization in Azure Level: Intermediate 200 Get an AI-powered view of your Azure workload health and performance while uncovering cost and carbon savings. In this lab, use AI to investigate anomalies, correlate telemetry, and drive optimization. Apply FinOps and sustainability insights, align health with SLI/SLO targets, and improve monitoring posture for lasting efficiency. Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees. Tuesday November 18 th , 2:45 PM PST LAB520: Start, Get and Stay Resilient with Azure Level: Intermediate 200 Understand the Start, Get, and Stay Resilient journey. Get equipped with tools & insights to architect mission critical applications with Azure’s Resiliency and Configuration experiences. Assess your resiliency posture, apply recommendations, validate your posture and orchestrate recovery. With the Essentials Machine Management bundle from Azure, manage and maintain the state of your resources, enforce configurations across devices and ensure resilience is not a one-time goal but an ongoing state. Please RSVP and arrive at least 5 minutes before the start time, at which point remaining spaces are open to standby attendees. Tuesday, November 18 th , 4:30 PM PST
Performance and Scalability of Azure HBv5-series Virtual Machines
Azure HBv5-series virtual machines (VMs) for CPU-based high performance computing (HPC) are now Generally Available. This blog provides in-depth information about the technical underpinnings, performance, cost, and management implications of these HPC-optimized VMs. Azure HBv5 VM bring leadership levels of performance, cost optimization, and server (VM) consolidation for a variety of workloads driven by memory performance, such as computational fluid dynamics, weather simulation, geoscience simulations, and finite element analysis. For these applications and compared to HBv4 VMs, previously the highest performance offering for these workloads, HBv5 provides up to : 5x higher performance for CFD workloads with 43% lower costs 3.2x higher performance for weather simulation with 16% lower costs 2.8x higher performance for geoscience workloads at the same costs HBv5-series Technical Overview & VM Sizes Each HBv5 VMs features several new technologies for HPC customers, including: Up to 6.6 TB/s of memory bandwidth (STREAM TRIAD) and 432 GB memory capacity Up to 368 physical cores per VM (user configurable) with custom AMD EPYC CPUs, Zen4 microarchitecture (SMT disabled) Base clock of 3.5 GHz (~1 GHz higher than other 96-core EPYC CPUs), and Boost clock of 4 GHz across all cores 800 Gb/s NVIDIA Quantum-2 InfiniBand (4 x 200 Gb/s CX-7) (~2x higher HBv4 VMs) 180 Gb/s Azure Accelerated Networking (~2.2 higher than HBv4 VMs) 15 TB local NVMe SSD with up to 50 GB/s (read) and 30 GB/s (write) of bandwidth (~4x higher than HBv4 VMs) The highlight feature of HBv5 VMs is their use of high-bandwidth memory (HBM). HBv5 VMs utilize a custom AMD CPU that increases memory bandwidth by ~9x v. dual-socket 4 th Gen EPYC (Zen4, “Genoa”) server platforms, and ~7x v. dual-socket EPYC (Zen5, “Turin”) server platforms, respectively. HBv5 delivers similar levels of memory bandwidth improvement compared to the highest end alternatives from the Intel Xeon and ARM CPU ecosystems. HBv5-series VMs are available in the following sizes with specifications as shown below. Just like existing H-series VMs, HBv5-series includes constrained cores VM sizes, enabling customers to optimize their VM dimensions for a variety of scenarios: ISV licensing constraining a job to a targeted number of cores Maximum-performance-per-VM or maximum performance per core Minimum RAM/core (1.2 GB, suitable for strong scaling workloads) to maximum memory per core (9 GB, suitable for large datasets and weak scaling workloads Table 1: Technical specifications of HBv5-series VMs Note: Maximum clock frequencies (FMAX) are based product specifications of the AMD EPYC 9V64H processor. Experienced clock frequencies by a customer are a function of a variety of factors, including but not limited to the arithmetic intensity (SIMD) and parallelism of an application. For more information see official documentation for HBv5-series VMs Microbenchmark Performance This section focuses on microbenchmarks that characterize performance of the memory subsystem, compute capabilities, and InfiniBand network of HBv5 VMs. Memory & Compute Performance To capture synthetic performance, we ran the following industry standard benchmarks: STREAM – memory bandwidth High Performance Conjugate Gradient (HPCG) – sparse linear algebra High Performance Linpack (HPL)– dense linear algebra Absolute results and comparisons to HBv4 VMs are shown in Table 2, below: Table 2: Results of HBv5 running the STREAM, HPCG, and HPL benchmarks. Note: STREAM was run with the following CLI parameters: OMP_NUM_THREADS=368 OMP_PROC_BIND=true OMP_PLACES=cores ./amd_zen_stream STREAM data size: 2621440000 bytes InfiniBand Networking Performance Each HBv5-series VM is equipped with four NVIDIA Quantum-2 network interface cards (NICs), each operating at 200 Gb/s for an aggregate bandwidth of 800 Gb/s per VM (node). We ran the industry standard IB perftests based on OSU benchmarks test across two (2) HBv5-series VMs, as depicted in the results shown in Figures 3-5, below: Note: all results below are for a single 200 Gb/s (uni-directional) link only. At a VM level, all bandwidth results below are 4x higher as there are four (4) InfiniBand links per HBv5 server. Unidirectional bandwidth: numactl -c 0 ib_send_bw -aF -q 2 Figure 1: results showing 99% achieved uni-directional bandwidth v. theoretical peak. Bi-directional bandwidth: numactl -c 0 ib_send_bw -aF -q 2 -b Figure 2: results showing 99% achieved bi-directional bandwidth v. theoretical peak. Latency: Figure 3: results measuring as low as 1.25 microsecond latencies among HBv5 VMs. Latencies experienced by users will depend on message sizes employed by applications. Application Performance, Cost/Performance, and Server (VM) Consolidation This section focuses on characterizing HBv5-series VMs when running common, real-world HPC applications with an emphasis on those known to be meaningfully bound by memory performance as that is the focus of the HB-series family. We characterize HBv5 below in three (3) ways of high relevance to customer interests: Performance (“how much faster can it do the work”) Cost/Performance (“how much can it reduce the costs to complete the work”) Fleet consolidation (“how much can a customer simplify the size and scale of compute fleet management while still being able to the work”) Where possible, we have included comparisons to other Azure HPC VMs, including: Azure HBv4/HX series with 176 physical cores of 4 th Gen AMD EPYC CPUs with 3D V-Cache (“Genoa-X”) (HBv4 specifications, HX specifications) Azure HBv3 with 120 physical cores of 3 rd Gen AMD EPYC CPUs with 3D V-Cache (“Milan-X”) (HBv3 specifications) Azure HBv2 with 120 physical cores of 2 nd Gen AMD EPYC CPUs (“Rome”) processors (full specifications) Unless otherwise noted, all tests shown below were performed with: Alma Linux 8.10 (image URN : almalinux:almalinux-hpc:8_10-hpc-gen2:latest) for scaling ( image URN: almalinux:almalinux-hpc:8_6-hpc-gen2:latest) NVIDIA HPC-X MPI Further, all Cost/Performance comparisons leverage pricing rate info from list price, Pay-As-You-Go (PAYG) information found on Azure Linux Virtual Machines Pricing. Absolute costs will be a function of a customer’s workload, model, and consumption (PAYG v. Reserved Instance, etc.) approach. That said, the relative cost/performance comparisons illustrated below should hold for the workload and model combinations shown below, regardless of the consumption approach. Computational Fluid Dynamics (CFD) OpenFOAM – version 2306 with 100M Cell Motorbike case Figure 4: HBv5 v. HBv4 on on OpenFOAM with the Motorbike 100M cell case HBv5 VMs provide a 4.8x performance increase over HBv4 VMs. Figure 5: The cost to complete the OpenFOAM Motorbike 100M case is just 57% of what it costs to complete the same case on HBv4. Above, we can see that for customers running OpenFOAM cases similar to the size and complexity of the 100M cell Motorbike problem, organizations can consolidate their server (VM) deployments by approximately a factor of five (5). Palabos – version 1.01 with 3D Cavity, 1001 x 1001 x 1001 cells case Figure 6: On Palabos, a Lattice Boltzmann solver using a streaming memory access pattern, HBv5 VMs provide a 4.4x performance increase over HBv4 VMs. Figure 7: The cost to complete the Palabos 3D Cavity case is just 62% of what it costs to complete the same case on HBv4. Above, we can see that for customers running Palabos with cases similar to the size and complexity of the 100M cell Motorbike problem, organizations can consolidate their server (VM) deployments by approximately a factor of ~4.5. Ansys Fluent – version 2025 R2 with F1 Racecar 140M case Figure 8: On ANSYS Fluent HBv5 VMs provide a 3.4x performance increase over HBv4 VMs. Figure 9: The cost to complete the ANSYS Fluent F1 racecar 140M case is just 81% of what it costs to complete the same case on HBv4. Above, we can see that for customers running ANSYS Fluent with cases similar to the size and complexity of the 140M cell F1 Racecar problem, organizations can consolidate their server (VM) deployments by approximately a factor of ~3.5. Siemens Star-CCM+ - version 17.04.005 with AeroSUV Steady Coupled 106M case Figure 10: On Star-CCM+, HBv5 VMs provide a 3.4x performance increase over HBv4 VMs. Figure 11: The cost to complete the Siemens Star-CCM+ANSYS Fluent F1 racecar 140M case is just 81% of what it costs to complete the same case on HBv4. Above, we can see that for customers running Star-CCM+ with cases similar to the size and complexity of the 106M cell AeroSUV Steady Coupled, organizations can consolidate their server (VM) deployments by approximately a factor of ~3.5. Weather Modeling WRF – version 4.2.2 with CONUS 2.5KM case Figure 12: On WRF, HBv5 VMs provide a 3.27x performance increase over HBv4 VMs. Figure 13: The cost to complete the WRF Conus 2.5KM case is just 84% of what it costs to complete the same case on HBv4. Above, we can see that for customers running WRF with cases similar to the size and complexity of the 2.5km CONUS, organizations can consolidate their server (VM) deployments by approximately a factor of ~3. Energy Research Devito – version 4.8.7 with Acoustic Forward case Figure 14: On Devito, HBv5 VMs provide a 3.27x performance increase over HBv4 VMs. Figure 15: The cost to complete the Devito Acoustic Forward OP case is equivalent to what it costs to complete the same case on HBv4. Above, we can see that for customers running Devito with cases similar to the size and complexity of the Acoustic Forward OP, organizations can consolidate their server (VM) deployments by approximately a factor of ~3. Molecular Dynamics NAMD - version 2.15a2 with STMV 20M case Figure 16: On NAMD, HBv5 VMs provide a 2.18x performance increase over HBv4 VMs. Figure 17: The cost to complete the NAMD STMV 20M case is 26% higher on HBv5 than what it costs to complete the same case on HBv4 Above, we can see that for customers running NAMD with cases similar to the size and complexity of the STMV 20M case, organizations can consolidate their server (VM) deployments by approximately a factor of ~2. Notably, NAMD is a compute bound case, rather than memory performance bound. We include it here to illustrate that not all workloads are fit for purpose with HBv5. This latest Azure HPC VM is the fastest at this workload on the Microsoft Cloud, but does not benefit substantially from HBv5’s premium levels of memory bandwidth. NAMD would instead perform more cost efficiently with a CPU that supports AVX512 instructions natively or, much better still, a modern GPU. Scalability of HBv5-series VMs Weak Scaling Weak scaling measures how well a parallel application or system performs when both the number of processing elements and the problem size increase proportionally, so that the workload per processor remains constant. Weak scaling cases are often employed when time-to-solution is fixed (e.g. it is acceptable to solve a problem within a specified period) but a user desires a simulation to be of a higher fidelity or resolution. A common example is operational weather forecasting. To illustrate weak scaling on HBv5 VMs, we ran Palabos with the same 3D cavity problem as shown earlier: Figure 18: On Palabos with the 3D Cavity model, HBv5 scales linearly as the 3D cavity size is proportionately increased. Strong Scaling Strong scaling is characterized by the efficiency with which execution time is reduced as the number of processor elements (CPUs, GPUs, etc.) is increased, while the problem size remains kept constant. Strong scaling cases are often employed when the fidelity or resolution of the simulation is acceptable, but a user requires faster time to completion. A common example is product engineering validation when an organization wants to bring a product to market faster but must complete a broad range of validation and verification scenarios before doing so. To illustrate Strong scaling on HBv5 VMs, we ran NAMD with two different problems, each intended to illustrate the how expectations for strong scaling efficiency change depending on problem size and the ordering of computation v. communication in distributed memory workloads. First, let us examine NAMD with the 20M STMV benchmark Figure 19: On NAMD with the STMV 20M cell case, HBv5 scales linearly as the 3D cavity size is proportionately increased. As illustrated above, for strong scaling cases for which the compute time is continuously reduced (by leveraging more and more processor elements) but communication time remains constant, scaling efficiency will only stay high for so long. That principle is well-represented by the STMV 20m case, for which parallel efficiency remains linear (i.e. cost/job remains flat) at two (2) nodes but degrades after that. This is because while compute is being sped up, the MPI time remains relatively flat. As such, the relatively static MPI time comes to dominate end-to-end wall clock time as VM scaling increases. Said another way, HBv5 features so much compute performance that even for a moderate-sized problem like STMV 20M scaling the infrastructure can only take performance so far and cost/job will begin to increase. If we examine HBv5 against the 210M cell case, however, with 10.5x as many elements to compute as its 20M case sibling, the scaling efficiency story changes significantly. Figure 19: On NAMD with the STMV 210M cell case, HBv5 scales linearly out to 32 VMs (or more than 11,000 CPU cores). As illustrated above, larger cases with significant compute requirements will continue to scale efficiently with larger amounts of HBv5 infrastructure. While MPI time remains relatively flat for this case (as is the case with the smaller STMV 20M case), the compute demands remain the dominant fraction of end-to-end wall clock time. As such, HBv5 scales these problems with very high levels of efficiency and in doing so job costs to the user remain flat despite up to 8x as many VMs being leveraged compared to the four (4) VM baseline. The key takeaways for strong scaling scenarios are two-fold. First, users should run scaling tests with their applications and models to find a sweet spot of faster performance with constant job costs. This will depend heavily on model size. Second, as new and very high end compute platforms like HBv5 emerge that accelerate compute time, application developers will need to find ways reduce wall clock times bottlenecking on communication (MPI) time. Recommended approaches include using fewer MPI processes and, ideally, restructuring applications to overlap communication with compute phases.Boosting Performance with the Latest Generations of Virtual Machines in Azure
Microsoft Azure recently announced the availability of the new generation of VMs (v6)—including the Dl/Dv6 (general purpose) and El/Ev6 (memory-optimized) series. These VMs are powered by the latest Intel Xeon processors and are engineered to deliver: Up to 30% higher per-core performance compared to previous generations. Greater scalability, with options of up to 128 vCPUs (Dv6) and 192 vCPUs (Ev6). Significant enhancements in CPU cache (up to 5× larger), memory bandwidth, and NVMe-enabled storage. Improved security with features like Intel® Total Memory Encryption (TME) and enhanced networking via the new Microsoft Azure Network Adaptor (MANA). By Microsoft Evaluated Virtual Machines and Geekbench Results The table below summarizes the configuration and Geekbench results for the two VMs we tested. VM1 represents a previous-generation machine with more vCPUs and memory, while VM2 is from the new Dld e6 series, showing superior performance despite having fewer vCPUs. VM1 features VM1 - D16S V5 (16 Vcpus - 64GB RAM) VM1 - D16S V5 (16 Vcpus - 64GB RAM) VM2 features VM2 - D16ls v6 (16 Vcpus - 32GB RAM) VM2 - D16ls v6 (16 Vcpus - 32GB RAM) Key Observations: Single-Core Performance: VM2 scores 2013 compared to VM1’s 1570, a 28.2% improvement. This demonstrates that even with half the vCPUs, the new Dld e6 series provides significantly better performance per core. Multi-Core Performance: Despite having fewer cores, VM2 achieves a multi-core score of 12,566 versus 9,454 for VM1, showing a 32.9% increase in performance. VM 1 VM 2 Enhanced Throughput in Specific Workloads: File Compression: 1909 MB/s (VM2) vs. 1654 MB/s (VM1) – a 15.4% improvement. Object Detection: 2851 images/s (VM2) vs. 1592 images/s (VM1) – a remarkable 79.2% improvement. Ray Tracing: 1798 Kpixels/s (VM2) vs. 1512 Kpixels/s (VM1) – an 18.9% boost. These results reflect the significant advancements enabled by the new generation of Intel processors. Score VM 1 VM 1 VM 1 Score VM 2 VM 2 VM 2 Evolution of Hardware in Azure: From Ice Lake-SP to Emerald Rapids Technical Specifications of the Processors Evaluated Understanding the dramatic performance improvements begins with a look at the processor specifications: Intel Xeon Platinum 8370C (Ice Lake-SP) Architecture: Ice Lake-SP Base Frequency: 2.79 GHz Max Frequency: 3.5 GHz L3 Cache: 48 MB Supported Instructions: AVX-512, VNNI, DL Boost VM 1 Intel Xeon Platinum 8573C (Emerald Rapids) Architecture: Emerald Rapids Base Frequency: 2.3 GHz Max Frequency: 4.2 GHz L3 Cache: 260 MB Supported Instructions: AVX-512, AMX, VNNI, DL Boost VM 2 Impact on Performance Cache Size Increase: The jump from 48 MB to 260 MB of L3 cache is a key factor. A larger cache reduces dependency on RAM accesses, thereby lowering latency and significantly boosting performance in memory-intensive workloads such as AI, big data, and scientific simulations. Enhanced Frequency Dynamics: While the base frequency of the Emerald Rapids processor is slightly lower, its higher maximum frequency (4.2 GHz vs. 3.5 GHz) means that under load, performance-critical tasks can benefit from this burst capability. Advanced Instruction Support: The introduction of AMX (Advanced Matrix Extensions) in Emerald Rapids, along with the robust AVX-512 support, optimizes the execution of complex mathematical and AI workloads. Efficiency Gains: These processors also offer improved energy efficiency, reducing the energy consumed per compute unit. This efficiency translates into lower operational costs and a more sustainable cloud environment. Beyond Our Tests: Overview of the New v6 Series While our tests focused on the Dld e6 series, Azure’s new v6 generation includes several families designed for different workloads: 1. Dlsv6 and Dldsv6-series Segment: General purpose with NVMe local storage (where applicable) vCPUs Range: 2 – 128 Memory: 4 – 256 GiB Local Disk: Up to 7,040 GiB (Dldsv6) Highlights: 5× increased CPU cache (up to 300 MB) and higher network bandwidth (up to 54 Gbps) 2. Dsv6 and Ddsv6-series Segment: General purpose vCPUs Range: 2 – 128 Memory: Up to 512 GiB Local Disk: Up to 7,040 GiB in Ddsv6 Highlights: Up to 30% improved performance over the previous Dv5 generation and Azure Boost for enhanced IOPS and network performance 3. Esv6 and Edsv6-series Segment: Memory-optimized vCPUs Range: 2 – 192* (with larger sizes available in Q2) Memory: Up to 1.8 TiB (1832 GiB) Local Disk: Up to 10,560 GiB in Edsv6 Highlights: Ideal for in-memory analytics, relational databases, and enterprise applications requiring vast amounts of RAM Note: Sizes with higher vCPUs and memory (e.g., E128/E192) will be generally available in Q2 of this year. Key Innovations in the v6 Generation Increased CPU Cache: Up to 5× more cache (from 60 MB to 300 MB) dramatically improves data access speeds. NVMe for Storage: Enhanced local and remote storage performance, with up to 3× more IOPS locally and the capability to reach 400k IOPS remotely via Azure Boost. Azure Boost: Delivers higher throughput (up to 12 GB/s remote disk throughput) and improved network bandwidth (up to 200 Gbps for larger sizes). Microsoft Azure Network Adaptor (MANA): Provides improved network stability and performance for both Windows and Linux environments. Intel® Total Memory Encryption (TME): Enhances data security by encrypting the system memory. Scalability: Options ranging from 128 vCPUs/512 GiB RAM in the Dv6 family to 192 vCPUs/1.8 TiB RAM in the Ev6 family. Performance Gains: Benchmarks and internal tests (such as SPEC CPU Integer) indicate improvements of 15%–30% across various workloads including web applications, databases, analytics, and generative AI tasks. My personal perspective and point of view The new Azure v6 VMs mark a significant advancement in cloud computing performance, scalability, and security. Our Geekbench tests clearly show that the Dld e6 series—powered by the latest Intel Xeon Platinum 8573C (Emerald Rapids)—delivers up to 30% better performance than previous-generation machines with more resources. Coupled with the hardware evolution from Ice Lake-SP to Emerald Rapids—which brings a dramatic increase in cache size, improved frequency dynamics, and advanced instruction support—the new v6 generation sets a new standard for high-performance workloads. Whether you’re running critical enterprise applications, data-intensive analytics, or next-generation AI models, the enhanced capabilities of these VMs offer significant benefits in performance, efficiency, and cost-effectiveness. References and Further Reading: Microsoft’s official announcement: Azure Dld e6 VMs Internal tests performed with Geekbench 6.4.0 (AVX2) in the Germany West Central Azure region.Breaking the Million-Token Barrier: The Technical Achievement of Azure ND GB300 v6
Azure ND GB300 v6 Virtual Machines with NVIDIA GB300 NVL72 rack-scale systems achieve unprecedented performance of 1,100,000 tokens/s on Llama2 70B Inference, beating the previous Azure ND GB200 v6 record of 865,000 tokens/s by 27%.
Take Data Management to the next level with Silk Software-Defined Azure Storage
Note: This article is co-authored by our partner Silk. In today’s data-driven world, every enterprise is under pressure to make smarter decisions faster. Whether you're running production databases, training machine learning models, running advanced analytics, or building customer-facing applications, your data needs to be more agile, secure, and readily accessible than ever before. That’s where Silk’s software-defined cloud storage platform on Microsoft Azure comes into play — bringing performance, resiliency, and intelligence to data management across the cloud. With the recent addition of Silk Echo, you can now supercharge your Copy Data Management (CDM) strategy to ensure your data isn’t just protected, it’s available instantly for any purpose — a true strategic asset. Transforming Azure IaaS with Silk's Platform Microsoft Azure offers a rich ecosystem of services to support every stage of your cloud journey, and when paired with Silk, customers gain a game-changing storage and data-services layer purpose-built for performance-intensive workloads. Silk’s software-defined storage platform runs on Azure infrastructure as a high-performance data layer between Azure compute and native storage. It works by orchestrating redundant sets of resources, as close to the DB compute as possible. Aggregating and accelerating the native capabilities of Azure, enabling databases to reach the maximum physical limits of the underlying hardware. Silk makes use of the excellent L-series of storage optimized VMs and NVME media, ensuring the data is always quickly available and able to withstand multiple failures using erasure coding. Silk is designed to address common challenges customers face when migrating relational databases — such as SQL Server, Oracle, and DB2 — to the cloud: Performance Bottlenecks: On-prem workloads often rely on ultra-low latency, high-throughput storage systems with features that are difficult to replicate in the cloud. Data Copy Sprawl: Multiple non-production environments (dev, test, QA, analytics) mean many redundant data copies, leading to storage inefficiencies. Operational Overhead: Managing snapshots, backups, and refreshes across environments consumes time and resources. Silk changes the game with: Extreme performance in the Azure cloud. Up to 34GB/s of throughput from a single VM. The combination of Silk and Azure provide a unique cost/performance balance, through the combination of a very low latency software defined cloud storage platform and sharing of Azure resources. Inline data deduplication and compression for optimized resource utilization. Autonomous, fully integrated, non-disruptive, zero cost snapshots and clones for effortless environment refreshes. Multi-zone resilience and no single point of failure. This makes it easier than ever to lift and shift critical applications to Azure, with the confidence of consistent performance, uptime, and flexibility. Elevating CDM with Silk Echo for AI While Silk’s core platform solves performance and efficiency challenges, Silk Echo for AI introduces an intelligent, AI-powered layer that revolutionizes how organizations manage and leverage their data across the enterprise. At its core, Silk Echo for AI offers next generation Copy Data Management capabilities that empower IT teams to accelerate digital initiatives, reduce costs, and maximize the value of every data copy. Key Benefits of Silk Echo for AI Smarter Data Copying and Cloning Silk Echo leverages AI to understand data access patterns and recommends the optimal strategy for creating and managing data copies. Instead of manually managing snapshots, you can automate the entire workflow — ensuring the right data is in the right place at the right time. Instant, Space-Efficient Clones Using Silk’s advanced snapshot and cloning engine, Echo creates fully functional clones in seconds, consuming minimal additional storage resources. Teams can spin up dev/test environments instantly, accelerating release cycles and experimentation. Cross-Environment Data Consistency Echo ensures consistency across copies — whether you're cloning for testing, backup, or analytics — and with AI-driven monitoring, it can detect drift between environments and recommend synchronizations. Policy-Based Lifecycle Management Define policies for how long data copies should live, when to refresh them, and who has access. Echo automates the enforcement of these policies, reducing human error and ensuring compliance. Optimized Resource Consumption Silk Echo minimizes redundant data storage through smart deduplication, compression, and AI-driven provisioning — resulting in cost savings of 50% or more across large-scale environments. Enablement for AI/ML Workflows For data science teams, Silk Echo provides curated, up-to-date data clones without impacting production environments — essential for model training, experimentation, and validation. Real-World Use Case: Streamlining Dev/Test and AI Pipelines Consider Sentara Health, a healthcare provider migrating their EHR and SQL Server workloads to Azure. Before Silk, environment refreshes were time-consuming, often taking days or even weeks. With Silk Echo for AI, the same tasks will be completed in minutes. Now, development teams have self-service access to fresh clones of production data — enabling faster iteration and better testing outcomes. Meanwhile, their data science team leverages Echo’s snapshot automation to feed AI models with real-time, production-grade data clones without risking downtime or data corruption. All of this runs seamlessly on Azure, with Silk ensuring high performance and resilience at every step. Joint Value of Silk and Microsoft Together, Silk and Microsoft are unlocking a new level of agility and intelligence for enterprise data management: Data-as-a-Service: Give every team — DevOps, DataOps, AI/ML — access to the data they need, when they need it. Free snapshots democratize data so up-to-date copies can be made quickly for any team member who can benefit from it. AI-Ready Database Infrastructure: Your infrastructure evolves from a reactive model which is addressing problems as they arise (i.e. triggering responses on alerts), to a predictive model that utilizes AI/ML to forecast issues and mitigate them before they occur by learning patterns of behavior. Silk enables real-time AI inferencing, for business-critical agents that require access to up-to-date operational data. Reduced Costs, Improved ROI: Storage optimization, reduced manual overhead, and faster time to value — backed by Azure’s scalability and Silk’s performance. Accelerated Cloud Migrations: Achieve the enhanced scalability and flexibility of a cloud migration for your Tier 1 databases without refactoring. Get Started Ready to take your data management strategy to the next level? Explore how Silk’s software-defined storage and Silk Echo for AI can accelerate your transformation on Microsoft Azure. Whether you're modernizing legacy systems, building AI-driven applications, or simply trying to get more value from your cloud investments, Silk and Microsoft are here to help. By embracing the power of Silk’s software-defined storage and Silk Echo, organizations can finally make their data in the cloud work smarter, not harder. Contact Alliances@silk.us for a deeper dive on Silk!
Revolutionizing Reliability: Introducing the Azure Failure Prediction and Detection (AFPD) system
As part of the journey to consistently improve Azure reliability and platform stability, we launched Azure Failure Prediction & Detection (AFPD), Azure’s premiere shift-left reliability solution. AFPD became operational in 2024, unifying failure prediction, detection, mitigation, and remediation services into a single end-to-end system with the goal of preventing Azure Compute customer workload interruptions and repairing nodes at scale. AFPD builds upon previous reliability solutions such as Project Narya, adding new best practices and fleet health management capabilities on top of pre-existing failure prediction and mitigation capabilities. The end-to-end AFPD system has proven to further reduce the overall number of reboots by over 36% and allows for a proactive approach to maintaining the cloud. This system operates for all Azure Compute General Purpose, Specialized Compute, High Performance Computing (HPC)/Artificial Intelligence (AI) workloads and select Azure Storage scenarios. For a deeper dive, you can read the whitepaper here, which won Best Paper Award at the 2025 IEEE Cloud Summit!Explore HPC & AI Innovation: Microsoft + AMD at HPC Roundtable 2025
The HPC Roundtable 2025 in Turin brings together industry leaders, engineers, and technologists to explore the future of high-performance computing (HPC) and artificial intelligence (AI) infrastructure. Hosted by DoITNow, the event features Microsoft and AMD as key participants, with sessions highlighting real-world innovations such as Polestar’s adoption of Microsoft Azure HPC for Computer-Aided Engineering (CAE). Attendees will gain insights into cloud-native HPC, hybrid compute environments, and the convergence of simulation and machine learning. The roundtable offers networking opportunities, strategic discussions, and showcases how Microsoft Azure and AMD are accelerating engineering innovation and intelligent workloads in automotive and other industries.Azure Native Pure Storage Cloud brings the best of Pure and Azure to our customers
Pure Storage Cloud is the result of a tightly coupled integration effort between the Pure and Azure teams that brings Pure’s industry-leading advanced data services to our customers. Built on rock solid Azure infrastructure, Pure makes Azure even better!Announcing preview of new Azure Dasv7, Easv7, Fasv7-series VMs based on AMD EPYC™ ‘Turin’ processor
Today, Microsoft is announcing preview of the new Azure AMD-based Virtual Machines (VMs), powered by 5th Generation AMD EPYC™ (Turin) processors. The preview includes general purpose (Dasv7 & Dalsv7 series), memory-optimized (Easv7 series) and compute-optimized (Fasv7, Falsv7, Famsv7 series) VMs, available with and without local disks. These VMs are in preview in the following Azure regions: East US 2, North Europe, and West US 3. To request access to the preview, please fill out the Preview-Signup. The latest Azure AMD-based VMs deliver significant enhancements over the previous generation (v6) AMD-based VMs: improved CPU performance, greater scalability, and expanded configuration options to meet the needs of a wide range of workloads. Key improvements include: Up to 35% CPU performance improvement compared to equivalent sized (v6) AMD-based VMs. Significant performance gains on other workloads: Up to 25% for Java-based workloads Up to 65% for in-memory cache applications Up to 80% for crypto workloads Up to 130% for web server applications Maximum boost CPU frequency of 4.5 GHz, enabling faster operations for compute-intensive workloads. Expanded VM sizes: Dasv7-series, Dalsv7-series and Easv7-series now scale up to 160 vCPUs. Fasv7-series supports up to 80 vCPUs, with a new 1-core size. Increased memory capacity: Dasv7-series now offers up to 640 GiB of memory. Easv7-series scales up to 1280 GiB and is ideal for memory-intensive applications. Enhanced remote storage performance: VMs offer up to 20% higher IOPS and up to 50% greater throughput compared to similar sized previous generation (v6) VMs. New VM families introduced: Fadsv7, Faldsv7, and Famdsv7 are now available with local disk support. Expanded constrained-core offerings: New constrained-core sizes for Easv7 and Famsv7, available with and without local disks, helping to optimize licensing costs for core-based software licensing. These enhancements make these latest VMs a compelling choice for customers seeking high performance, cost efficiency, and workload flexibility on Azure. Additionally, these VMs leverage the latest Azure Boost technology enhancements to performance and security of these new VMs. The new VMs utilize the Microsoft Azure Network Adapter (MANA), a next-generation network interface that provides stable, forward-compatible drivers for Windows and Linux operating systems. These VMs also support the NVMe protocol for both local and remote disks. The 5th Generation AMD EPYC™ processor family, based on the newest ‘Zen 5’ core, provides enhanced capabilities for these new Azure AMD-based VM series such as AVX-512 with a full 512-bit data path for vector and floating-point operations, higher memory bandwidth, and improved instructions per clock compared to the previous generation. These updates provide increased throughput and ability to scale for compute-intensive tasks like AI and machine learning, scientific simulations, and financial analytics, among others. AMD Infinity Guard hardware-based security features, such as Transparent Secure Memory Encryption (TSME), continue in this generation to ensure sensitive information remains secure. These VMs support three memory (GiB)-to-vCPU ratios such as 2:1 (Dalsv7-series, Daldsv7-series, Falsv7-series and Faldsv7-series), 4:1 (Dasv7-series, Dadsv7-series, Fasv7-series and Fadsv7-series), and 8:1 (Easv7-series, Eadsv7-series, Famsv7-series and Famdsv7-series). The Dalsv7-series are ideal for workloads that require less RAM per vCPU that can reduce costs when running non-memory intensive applications, including web servers, video encoding, batch processing and more. The Dasv7-series VMs work well for many general computing workloads, such as e-commerce systems, web front ends, desktop virtualization solutions, customer relationship management applications, entry-level and mid-range databases, application servers, and more. The Easv7-series VMs are ideal for workloads such as memory-intensive enterprise applications, data warehousing, business intelligence, in-memory analytics, and financial transactions. The new Falsv7-series, Fasv7-series and Famsv7-series VM series do not have Simultaneous Multithreading (SMT), meaning a vCPU equals a full core, which makes these VMs well-suited for compute-intensive workloads needing the highest CPU performance, such as scientific simulations, financial modeling and risk analysis, gaming, and more. In addition to the standard sizes, the latest VM series are available in constrained-core sizes, with vCPU count constrained to one-half or one-quarter of the original VM size, giving you the flexibility to select the core and memory configuration that best fits your workloads. In addition to the new VM capabilities, the previously announced Azure Integrated HSM (Hardware Security Module), will be in Preview soon with the latest Azure AMD-based VMs. Azure Integrated HSM is an ephemeral HSM cache that enables secure key management within Azure virtual machines by ensuring that cryptographic keys remain protected inside a FIPS 140-3 Level 3-compliant boundary throughout their lifecycle. To explore this new feature, please sign up using the form provided below. These latest Azure AMD-based VMs will be charged during preview; pricing information will be shared with access to the VMs. Eligible new Azure customers can sign up for a free account and receive a $200 Azure credit. The new VMs support all remote disk types. To learn more about the disk types and their regional availability, please refer to Azure managed disk type. Disk storage is billed separately from virtual machines. You can learn more about these latest Azure AMD-based VMs by visiting the specification pages at Dasv7-series, Dadsv7-series, Dalsv7-series, Daldsv7-series, Easv7-series, Eadsv7-series, Fasv7-series, Fadsv7-series, Falsv7-series, Faldsv7-series, Famsv7-series and Famdsv7-series. The latest Azure AMD-based VMs provide options for your wide range of computing needs. Explore the new VMs today and discover how these VMs can enhance your workload performance and lower your costs. To request access to the preview, please fill out the Preview-Signup form. Have questions? Please reach us at Azure Support and our experts will be there to help you with your Azure journey.
