Azure VM Sizes and Types
Azure Virtual Machines (VMs) come in various sizes and types to cater to different workloads and performance requirements. Here is an overview of Azure VM sizes and types as of my knowledge cutoff in September 2021. Please note that Azure offerings are regularly updated, and there might be new VM sizes and types available beyond this date.
General Purpose:
B-series: Economical burstable VMs optimized for workloads with variable CPU usage.
D-series: Balanced CPU-to-memory ratio for a wide range of applications.
Av2-series: Cost-effective VMs with a balanced CPU-to-memory ratio and solid-state drives (SSDs).
Compute Optimized:
F-series: Compute optimized with a higher CPU-to-memory ratio for high-performance applications.
Fs-series: Similar to F-series but with local temporary storage (SSD) for faster data access.
Memory Optimized:
E-series: Memory-optimized VMs with high memory-to-core ratios for in-memory applications.
M-series: Largest memory-optimized VMs available in Azure, designed for heavy in-memory workloads.
Storage Optimized:
Ls-series: High-throughput, low-latency VMs optimized for demanding storage workloads.
Lsv2-series: Similar to Ls-series but with larger SSD capacity and faster disk throughput.
GPU Instances:
NV-series: VMs with NVIDIA GPUs, suitable for GPU-accelerated workloads like AI, deep learning, and visualization.
NC-series: VMs with NVIDIA GPUs, optimized for high-performance computing (HPC) and AI workloads.
ND-series: VMs with NVIDIA GPUs and high-performance CPU, designed for AI and deep learning scenarios.
High-Performance Computing (HPC):
H-series: VMs designed for high-performance computing workloads, with RDMA (Remote Direct Memory Access) and InfiniBand support.
Confidential Computing:
DC-series: VMs that leverage hardware-based trusted execution environments (TEEs) for secure execution of sensitive workloads.
General Purpose:
B-series: These VMs offer a cost-effective solution for workloads with variable CPU usage. They provide burstable performance, meaning they accumulate CPU credits during periods of low CPU usage, which can be used for burstable performance during peak times.
D-series: D-series VMs provide a balance between CPU and memory resources, making them suitable for a wide range of applications. They offer a good combination of computing power and memory capacity.
Av2-series: Av2-series VMs are a cost-effective general-purpose option with a balanced CPU-to-memory ratio. They also include SSDs for faster storage performance.
Compute Optimized:
F-series: F-series VMs are optimized for compute-intensive workloads. They have a higher CPU-to-memory ratio, making them suitable for applications that require substantial processing power.
Fs-series: Similar to F-series, Fs-series VMs are designed for compute-intensive workloads but also include local temporary storage (SSD). The local SSD storage provides faster access to data, benefiting applications with high I/O requirements.
Memory Optimized:
E-series: E-series VMs are memory-optimized, providing a high memory-to-core ratio. They are suitable for memory-intensive workloads, such as in-memory databases or analytics applications.
M-series: M-series VMs are the largest memory-optimized VMs available in Azure. They offer a significant amount of memory, making them suitable for extremely memory-intensive workloads.
Storage Optimized:
Ls-series: Ls-series VMs are optimized for storage-intensive workloads. They provide high throughput and low latency storage performance, making them suitable for scenarios that require large amounts of data to be processed quickly.
Lsv2-series: Similar to Ls-series, Lsv2-series VMs are designed for storage-intensive workloads but offer larger SSD capacity and faster disk throughput, providing improved storage performance.
GPU Instances:
NV-series: NV-series VMs come with NVIDIA GPUs and are ideal for GPU-accelerated workloads such as AI, deep learning, and visualization. They provide powerful hardware acceleration for computationally intensive tasks.
NC-series: NC-series VMs are optimized for high-performance computing (HPC) and AI workloads. They feature NVIDIA GPUs and provide excellent GPU-to-CPU ratios.
ND-series: ND-series VMs also include NVIDIA GPUs along with high-performance CPUs. They are specifically designed for AI and deep learning scenarios that require significant computational power.
High-Performance Computing (HPC):
H-series: H-series VMs are tailored for high-performance computing workloads. They offer RDMA and InfiniBand support, which enables high-speed network communication between VMs for distributed computing scenarios.
7. Confidential Computing:
DC-series: DC-series VMs utilize hardware-based trusted execution environments (TEEs) for secure execution of sensitive workloads. They provide enhanced security features for scenarios that require protecting data during computation.
Conclusion:
Azure Virtual Machines (VMs) offer a diverse range of sizes and types to cater to various workload requirements. Here’s an overview of the different VM categories:
1. General Purpose: B-series for cost-effective burstable performance, D-series for a balanced CPU-to-memory ratio, and Av2-series for a balance of cost-effectiveness and performance.
2. Compute Optimized: F-series for compute-intensive workloads and Fs-series with local SSD storage for improved I/O performance.
3. Memory Optimized: E-series for memory-intensive applications and M-series for extremely memory-intensive workloads.
4. Storage Optimized: Ls-series for storage-intensive workloads with high throughput and low latency, and Lsv2-series for enhanced storage performance with larger SSD capacity.
5. GPU Instances: NV-series, NC-series, and ND-series, all equipped with NVIDIA GPUs, catering to GPU-accelerated workloads, high-performance computing (HPC), AI, and deep learning scenarios.
6. High-Performance Computing (HPC): H-series VMs designed specifically for high-performance computing workloads with RDMA and InfiniBand support.
7. Confidential Computing: DC-series VMs offering hardware-based trusted execution environments (TEEs) for secure execution of sensitive workloads.
These VM sizes and types allow users to select the most suitable option based on their specific requirements for CPU, memory, storage, GPU acceleration, and security. However, it’s important to stay updated with the latest Azure documentation to ensure you have the most current information on VM sizes and types available.
