HBv2-series VMs are optimized for applications driven by memory bandwidth, such as fluid dynamics, finite element analysis, and reservoir simulation. HBv2 VMs feature 120 AMD EPYC 7742 processor cores, 4 GB of RAM per CPU core, and no simultaneous multithreading. Each HBv2 VM provides up to 340 GB/sec of memory bandwidth, and up to 4 teraFLOPS of FP64 compute.
HBv2-series VMs feature 200 Gb/sec Mellanox HDR InfiniBand. These VMs are connected in a non-blocking fat tree for optimized and consistent RDMA performance. These VMs support Adaptive Routing and the Dynamic Connected Transport (DCT, in additional to standard RC and UD transports). These features enhance application performance, scalability, and consistency, and usage of them is strongly recommended.
|Size||vCPU||Processor||Memory (GiB)||Memory bandwidth GB/s||Base CPU frequency (GHz)||All-cores frequency (GHz, peak)||Single-core frequency (GHz, peak)||RDMA performance (Gb/s)||MPI support||Temp storage (GiB)||Max data disks||Max Ethernet vNICs|
|Standard_HB120rs_v2||120||AMD EPYC 7V12||480||350||2.45||3.1||3.3||200||All||480 + 960||8||8|
Size table definitions
Storage capacity is shown in units of GiB or 1024^3 bytes. When you compare disks measured in GB (1000^3 bytes) to disks measured in GiB (1024^3) remember that capacity numbers given in GiB may appear smaller. For example, 1023 GiB = 1098.4 GB.
Disk throughput is measured in input/output operations per second (IOPS) and MBps where MBps = 10^6 bytes/sec.
Data disks can operate in cached or uncached modes. For cached data disk operation, the host cache mode is set to ReadOnly or ReadWrite. For uncached data disk operation, the host cache mode is set to None.
If you want to get the best performance for your VMs, you should limit the number of data disks to two disks per vCPU.
Expected network bandwidth is the maximum aggregated bandwidth allocated per VM type across all NICs, for all destinations. For more information, see Virtual machine network bandwidth.
Upper limits aren't guaranteed. Limits offer guidance for selecting the right VM type for the intended application. Actual network performance will depend on several factors including network congestion, application loads, and network settings. For information on optimizing network throughput, see Optimize network throughput for Azure virtual machines. To achieve the expected network performance on Linux or Windows, you may need to select a specific version or optimize your VM. For more information, see Bandwidth/Throughput testing (NTTTCP).
- General purpose
- Memory optimized
- Storage optimized
- GPU optimized
- High performance compute
- Previous generations
- Learn more about configuring your VMs, enabling InfiniBand, setting up MPI and optimizing HPC applications for Azure at HPC Workloads.
- Read about the latest announcements and some HPC examples and results at the Azure Compute Tech Community Blogs.
- For a higher level architectural view of running HPC workloads, see High Performance Computing (HPC) on Azure.
- Learn more about how Azure compute units (ACU) can help you compare compute performance across Azure SKUs.