Applies to: ✔️ Linux VMs ✔️ Windows VMs ✔️ Flexible scale sets ✔️ Uniform scale sets
HC-series VMs are optimized for applications driven by dense computation, such as implicit finite element analysis, molecular dynamics, and computational chemistry. HC VMs feature 44 Intel Xeon Platinum 8168 processor cores, 8 GB of RAM per CPU core, and no hyperthreading. The Intel Xeon Platinum platform supports Intel’s rich ecosystem of software tools such as the Intel Math Kernel Library and advanced vector processing capabilities such as AVX-512.
HC-series VMs feature 100 Gb/sec Mellanox EDR 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 their usage is recommended.
Premium Storage: Supported
Premium Storage caching: Supported
Ultra Disks: Supported (Learn more about availability, usage and performance)
Live Migration: Not Supported
Memory Preserving Updates: Not Supported
VM Generation Support: Generation 1 and 2
Accelerated Networking: Supported (Learn more about performance and potential issues)
Ephemeral OS Disks: Supported
|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_HC44rs||44||Intel Xeon Platinum 8168||352||191||2.7||3.4||3.7||100||All||700||4||8|
Learn more about the:
- Architecture and VM topology
- Supported software stack including supported OS
- Expected performance of the HC-series VM
- Overview of HPC on InfiniBand-enabled H-series and N-series VMs.
- Configuring VMs and supported OS and VM Images.
- Enabling InfiniBand with HPC VM images, VM extensions or manual installation.
- Setting up MPI, including code snippets and recommendations.
- Cluster configuration options.
- Deployment considerations.
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.
To learn how to get the best storage performance for your VMs, see Virtual machine and disk performance.
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).
Other sizes and information
- General purpose
- Memory optimized
- Storage optimized
- GPU optimized
- High performance compute
- Previous generations
Pricing Calculator : Pricing Calculator
For more information on disk types, see What disk types are available in Azure?
- Read about the latest announcements, HPC workload examples and performance results at the Azure Compute Tech Community Blogs.
- For a high-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.