Build and run everything from simple web apps to AI supercomputing by matching each workload to the right Azure VM in minutes.
Find and know exactly what you’re provisioning by understanding the naming format to see CPU type, memory, storage, and features before deployment to match what your app or workload needs. Use free tools like Azure Migrate to right-size and plan.
Matt McSpirit, Microsoft Azure expert, shows how to choose, size, and deploy workloads such as burstable web apps, massive in-memory databases, GPU-driven AI training, and high-performance scientific modeling, all with automatic scaling and confidential computing when needed.
Skip guesswork.
Decode VM families, processor types, and premium options in seconds. Check out Azure virtual machine options.
Boost performance.
Azure Boost offloads storage and networking so your VM CPU stays focused on workloads. Watch here.
Scale to thousands of VMs automatically.
Scale Sets or Compute Fleet — zero manual ops. See options to deploy your Azure VMs.
QUICK LINKS:
00:00 — Azure Virtual Machines
01:12 — Decode Azure VM Names
01:28 — Right-Size with Azure Migrate
02:15 — B series
02:45 — D series
03:23 — E series
04:14 — F series
04:29 — L series
05:01 — M series
05:23 — Constrained vCPU VMs
05:49 — H series
06:20 — N series
06:55 — Azure Boost
07:24 — Confidential VMs & Deploying your VMs
08:28 — Wrap up
Link References
Get started at https://aka.ms/VMAzure
Azure VM naming conventions at https://aka.ms/VMnames
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Video Transcript:
-If you’re not sure which Azure virtual machine is the right fit for your app or workload, in the next few minutes, I’ll break down your options. Azure, with its vast compute infrastructure spanning 70-plus global regions, gives you the flexibility to choose from one of the broadest selections of VM types among major cloud providers to run any workload. A portfolio of hundreds of VM options has been designed to scale to the performance, cost, and specific technical requirements your workload needs, while providing access to the latest CPU technologies from Intel, plus Microsoft’s custom Cobalt CPUs, with their power efficient, Arm-based design, integrated systems from our close partnership with AMD, and, of course, NVIDIA for the very latest in GPU innovation.
-And you can deploy Windows Server-based applications or use your favorite Linux distro on Azure. Our Azure-tuned Linux kernels incorporate new features and performance improvements at a faster cadence compared to default or generic kernels, meaning there’s no need to repackage your apps and services. And for SUSE and Red Hat Linux, our engineers are co-located for integrated support to streamline and accelerate the resolution of any issues you might encounter in the least amount of time.
-So let’s break down your core options to select the right VMs for your workloads. Azure VM families are optimized to run any size of workload, from general purpose to memory, compute, and storage intensive workloads, in addition to high-performance computing and AI scenarios. Now, when you’re looking at Azure VM sizes, there’s a structured format that helps you understand the characteristics of each VM. It starts with the family, followed by an optional subfamily, the number of vCPUs, the processor type and additive features, and the version of the VM. Importantly, an a signifies AMD-based processes and p signifies that the VM is powered by Arm-based processes. If you don’t see an a or a p, the underlying processor is Intel based. And then a set of lowercase letters represent additive features, for example, s for premium storage.
-Now, to learn more, you can refer to aka.ms/VMnames, and you can leverage free tools like Azure Migrate to assess the requirements of your on-prem workloads and right-size your infrastructure on Azure. Then your choice of VM will depend on the workload as each series has different characteristics. So let’s start with our entry level B-series, or Burstable VMs, which are useful for workloads that typically run at a low to moderate CPU baseline, but sometimes need to burst to significantly higher CPU utilization when the demand rises. An example would be a web front end, like a check in/check out application at a hotel where you need to plan for sporadic compute capacity to handle traffic spikes.
-That said, most of your general purpose workloads, such as app servers or small to medium relational databases, are best run on the D family of Azure virtual machines. These VMs offer the vCPUs, memory, and temporary storage to meet the requirements of most production workloads. Or you can opt for no local temporary data disc to reduce your TCO. The latest D-series VMs include fast, larger local NVMe SSD storage and are designed for applications that benefit from low-latency, high-speed local storage, such as applications that require fast reads and writes to temporary storage.
-Conversely, memory-optimized VM sizes that are part of our E series offer high memory-to-CPU ratios. These VMs are ideal for large relational databases, data analytics, as well as other large in-memory business-critical workloads and applications like SAP NetWeaver. Then, depending on your requirements, you can select E-series VM sizes that include large and fast local NVMe SSD disk storage for applications that benefit from low latency and high-speed local storage, or, again, the no local temporary data disk option is available to reduce your TCO. Now, for workloads demanding the cutting edge of network performance, such as network virtual appliances, large-scale e-commerce, and media processing, network optimized variants, identified by the n, are available for both general purpose D-series and memory optimized E-series VMs. Next, for compute-intensive applications, F and FX-series VMs have high vCPU performance and are great for compute-intensive workloads, such as video encoding and rendering, electronic design automation, and gaming applications.
-Alternatively, if you need to run Big Data, NoSQL databases, or large data warehousing solutions on Azure, L-series VMs are optimized for storage-intensive workloads and are built for the speed of data access And they feature ultra-fast, low-latency NVMe storage that’s physically mapped to the host. This local storage layer is ideal for handling temporary data with high throughput, making it perfect for storage-intensive workloads. These VMs give you access to terabytes of storage and millions of IOPS. Then taking things to the next level, the M-series VMs are designed for applications that process large amounts of data in memory. They offer the largest memory of any VM series on Azure. M-series VMs are ideal for extremely large databases or other applications like SAP HANA that benefit from massive memory footprints and extremely high vCPU counts. Also, because some software is charged on a per core basis, to reduce the cost of software licensing for memory and storage-intensive workloads, we also provide the option of constrained vCPU-capable VMs across a number of VM series. Some database workloads, for example, may not need as many cores.
-So, with this option, we limit the vCPU count all while leaving memory, storage, and I/O bandwidth unchanged. Next, switching gears for more specialized workloads, the H-series VMs support high-performance computing workloads where the speed of memory is critical, like scientific workloads such as weather forecasting where tracking dangerous storms requires the highest precision forecasts with precise computations at speed. And there are many more critical everyday workload examples where these VMs can apply, from designing safer cars or planes and buildings to cheaper energy and even to studying the fluid dynamics of, for example, beer.
-Adding to this, if high-performance computing power for graphics-intensive and parallel processing tasks is more your thing, the N-series family of VMs are GPU-enabled and are ideal for AI model training, inference workloads, along with model fine-tuning and distillation, as well as digital twins, video rendering, predictive analytics, and more. Combined with NVIDIA NVLink and InfiniBand technologies to connect multiple GPUs and CPUs, you can build your own multi-VM supercomputer for the most demanding tasks. Importantly, powering all the latest generation Azure VMs is a key infrastructure technology called Azure Boost, which enhances Azure virtual machine performance by offloading storage, networking, and host management tasks to purpose-built hardware and software, freeing up computing resources for workloads. At the same time, it enforces code integrity so that only verified code can run on the system as well as strict isolation of workloads for additional security.
-And, across a number of these VM options, if you need more security, Azure Confidential VMs and GPUs are backed by Intel TDX and AMD SEV-SNP technologies. These use hardware-based Trusted Execution Environments, TEEs, across both CPUs and GPUs to protect data while it’s being processed. These ensure only verified and authorized code can access sensitive data, which is ideal for secure collaboration in regulated industries like healthcare, finance, and government. These confidential VMs are available for the D, E, and N series.
-So now you know your options. As you go to deploy your VMs, you also have options to do so at scale. Azure Virtual Machine Scale Sets let you create thousands of virtual machines in one go and increase or decrease the number of VMs needed automatically based on load or schedule. And with another option, Azure Compute Fleet, you can launch even larger groups of VMs based directly on workload or cost requirements with options to select many VM types in the same fleet.
-So that was a quick tour of your options for Azure Core Compute. Whether you have basic or advanced compute needs, we give you a huge range of VMs to choose from. You can find more resources on the topic at aka.ms/VMAzure. And as always, thanks for watching.
