Virtual machine flavors and billing unit rates

This article lists the types (flavors) of virtual machines and their cost in billing units.

• Virtual machine flavors and billing unit rates
  • Flavor notation
    • Power redundancy
    • Data redundancy
    • Network redundancy
    • Other symbols
  • cPouta flavors
    • Standard flavors
    • HPC flavors
    • I/O flavors
    • GPU flavors
  • ePouta flavors
    • Standard flavors
    • HPC flavors
    • I/O flavors
    • High memory flavors
    • GPU flavors
  • Which type of flavor should I use?
    • Standard flavors
    • HPC flavors
    • I/O flavors
    • GPU flavors
    • High memory flavors (only in ePouta)
  • Deprecated flavors

The cPouta and ePouta services consume the same billing units as Puhti and Mahti. You can find more information in the CSC computing environment articles.

Users can create virtual machines with larger or smaller compute resources based on their needs. The virtual machine flavors available in cPouta and ePouta are listed below in separate tables. Please note that the values for the memory of each flavor (in GiB) are approximated.

Flavor notation

We use symbols to describe some of the features of the flavors we offer. A short descripion of the notation used follows.

Power redundancy

For the power provisioning of the node hosting the virtual machine, there are two possible values of redundancy.

• NONE - The node is not protected from sudden power losses. A fault in the power provisioning of the node might make the virtual machine temporarily unreachable.
• FULL - The node is protected from sudden power losses (UPS).

Data redundancy

Within each virtual machine, the customer data is stored in a root disk (R) and possibly in an ephemeral disk (E). For customer data, there are three possible values of redundancy.
We also offer the possibility to store the data in a persistent volume (FULL)

• NONE - The disk is stored only in the node running the virtual machine and it is not backed up (RAID-0 or LVM striping). A fault in one of the disks of the node might corrupt the data of the virtual machine. Moreover, a fault in the node hosting the virtual machine might make the virtual machine not usable until the fault is fixed.
• BASIC - The disk is stored only in the node running the virtual machine and it is mirrored within the same node (RAID-1). A fault in a single disk of the node does not compromise the data of the virtual machine. Simultaneous faults in multiple disks of the node might corrupt the data of the virtual machine. Moreover, a fault in the node hosting the virtual machine might make the virtual machine not usable until the fault is fixed.
• FULL - The disk is stored using multiple nodes in a fault-tolerant fashion (Ceph), so the customer data is not tied to any specific node. In case of a fault in a node used by the customer, it is possible to re-spawn the virtual machine of the customer using an alternative node.

Network redundancy

For the network reachability of the virtual machine, there are two possible values of redundancy.

• NONE - The node hosting the virtual machine is connected to the cloud platform without a failover link. A fault in the link of the node might make the virtual machine temporarily unreachable.
• FULL - The node hosting the virtual machine is connected to the cloud platform with an additional failover link.

Other symbols

• - Launching new virtual machines with this flavor is temporarily not possible. Existing virtual machines are not affected.

cPouta flavors

The following tables list the available virtual machine flavors in cPouta and their billing unit coefficients. Note that the default cPouta user account allows users to launch only a subset of the available virtual machine flavors.

Standard flavors

Flavor	Cores	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
standard.tiny	1	0.9	80	0	80	0.9		0.25
standard.small	2	1.9	80	0	80	0.9		0.5
standard.medium	3	3.9	80	0	80	1.3		1
standard.large	4	7.8	80	0	80	1.9		2
standard.xlarge	6	15	80	0	80	2.5		4
standard.xxlarge	8	31	80	0	80	3.8		8
standard.3xlarge	8	62	80	0	80	7.7		16

HPC flavors

Flavor	Cores	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
hpc.6.14core	14	88	80	0	80	6.2		23
hpc.6.28core	28	176	80	0	80	6.2		45
hpc.6.56core	56	352	80	0	80	6.2		90
hpc.6.112core	112	705	80	0	80	6.2		180
hpc.5.16core	16	58	80	0	80	3.6		20
hpc.5.32core	32	116	80	0	80	3.6		40
hpc.5.64core	64	232	80	0	80	3.6		80
hpc.5.128core	128	464	80	0	80	3.6		160
hpc.4.5core	5	21	80	0	80	4.2		6
hpc.4.10core	10	42	80	0	80	4.2		12
hpc.4.20core	20	85	80	0	80	4.2		25
hpc.4.40core	40	171	80	0	80	4.2		50
hpc.4.80core	80	343	80	0	80	4.2		100

I/O flavors

Flavor	Cores	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
io.70GB	2	9.7	20	70	90	4.8		3
io.160GB	4	19	20	160	180	4.7		6
io.340GB	8	39	20	340	360	4.8		12
io.700GB	16	78	20	700	720	4.8		24
io.2.80GB	2	12,7	80	80	160	6.3		6
io.2.240GB	4	26	80	240	320	6.6		12
io.2.550GB	8	54	80	550	630	6.7		24
io.2.1200GB	16	107	80	1200	1280	6.7		48

Note that both the root and the ephemeral disks of all I/O flavors are hosted on solid-state drives (SSDs).

GPU flavors

Flavor	Cores	GPUs	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
gpu.1.1gpu	14	1	117	80	0	80	8.3		60
gpu.1.2gpu	28	2	234	80	0	80	8.3		120
gpu.1.4gpu	56	4	468	80	0	80	8.3		240

Note that the root disks of all GPU flavors are hosted on solid-state drives (SSDs).

ePouta flavors

The following tables list the available virtual machine flavors in ePouta and their billing unit coefficients.

Standard flavors

Flavor	Cores	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
standard.tiny	1	0.9	80	0	80	0.9		0.25
standard.small	2	1.9	80	0	80	0.9		0.5
standard.medium	3	3.9	80	0	80	1.3		1
standard.large	4	7.8	80	0	80	1.9		2
standard.xlarge	6	15	80	0	80	2.5		4
standard.xxlarge	8	31	80	0	80	3.8		8
standard.3xlarge	8	62	80	0	80	7.7		16

HPC flavors

Flavor	Cores	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
hpc.6.14core	14	88	80	0	80	6.2		25
hpc.6.28core	28	176	80	0	80	6.2		50
hpc.6.56core	56	352	80	0	80	6.2		100
hpc.6.112core	112	705	80	0	80	6.2		200
hpc.5.16core	16	58	80	0	80	3.6		22.5
hpc.5.32core	32	116	80	0	80	3.6		45
hpc.5.64core	64	232	80	0	80	3.6		90
hpc.5.128core	128	464	80	0	80	3.6		180
hpc.4.5core	5	21	80	0	80	4.2		8
hpc.4.10core	10	43	80	0	80	4.3		15
hpc.4.20core	20	87	80	0	80	4.3		30
hpc.4.40core	40	175	80	0	80	4.3		60
hpc.4.80core	80	351	80	0	80	4.3		120

I/O flavors

Flavor	Cores	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
io.2.80GB	2	12,7	80	80	160	6.3		6
io.2.240GB	4	26	80	240	320	6.6		12
io.2.550GB	8	54	80	550	630	6.7		24
io.2.1200GB	16	107	80	1200	1280	6.7		48

High memory flavors

Flavor	Cores	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
tb.3.480RAM	56	480	20	1650	1730	8.5		110
tb.3.1470RAM	80	1470	80	2500	2580	18		320

Note that the root disks of all high memory flavors are hosted on solid-state drives (SSDs), while the ephemeral disks are hosted using NVM Express (NVMe).

GPU flavors

Flavor	Cores	GPUs	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
gpu.1.1gpu	14	1	117	80	0	80	8.3		60
gpu.1.2gpu	28	2	234	80	0	80	8.3		120
gpu.1.4gpu	56	4	468	80	0	80	8.3		240
gpu.2.1gpu	20	1	180	80	1000	1080	9		100
gpu.3.1gpu	12	1	219	80	1500	1580	18		150

Note that both the root and the ephemeral disks of the GPU flavors are hosted on solid-state drives (SSDs).

Which type of flavor should I use?

Standard flavors

Typical use cases:

• Web services (non-HPC)
• Software development

These are generic flavors that are useful for running regular web services such as a web server with a database backend. They provide better availability compared to the HPC flavors.

Cloud administrators can move these virtual machines from one host machine to another without causing a break in service. This means that you are likely less affected by maintenance.

These flavors are not suitable for computationally intensive workloads. The virtual CPUs used in these instances are overcommitted, which means 32 hyperthreaded CPU cores are used to provide more than 32 virtual cores.

Flavor characteristics:

• Redundant power
• CPU: Varies
• Network: Redundant 25 Gb/s
• Flavor disk: Stored in the central storage
• Single-node or disk failures may cause downtime, but instances are recoverable.

HPC flavors

Typical use cases:

• Scientific applications

If your use case is computationally intensive, you should use one of the HPC flavors. The availability of these instances is not as high as the standard flavors, but you get better performance. The HPC flavors have faster CPUs and no overcommitment of CPU cores.

cPouta HPC flavor characteristics:

hpc.6.*:

• Redundant power
• CPU: AMD EPYC 9734 112-Core Processor,
• Network: Redundant 25 Gb/s
• Flavor disk: Stored in the central storage
• Single-node failure may cause downtime, but instances are recoverable.

hpc.5.*:

• Redundant power
• CPU: AMD EPYC 7702 64-Core Processor,
• Network: Redundant 25 Gb/s
• Flavor disk: Stored in the central storage
• Single-node failure may cause downtime, but instances are recoverable.

hpc.4.*:

• No redundant power
• CPU: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, hyper-threading
• Network: Redundant 25 Gb/s
• Flavor disk: Stored in the central storage
• Single-node or disk failures may cause downtime, but instances are recoverable.

ePouta HPC flavor characteristics:

hpc.6.*:

• Redundant power
• CPU: AMD EPYC 9734 112-Core Processor,
• Network: Redundant 25 Gb/s
• Flavor disk: Stored in the central storage
• Single-node failure may cause downtime, but instances are recoverable.

hpc.5.*:

• Redundant power
• CPU: AMD EPYC 7702 64-Core Processor,
• Network: Redundant 25 Gb/s
• Flavor disk: Stored in the central storage
• Single-node failure may cause downtime, but instances are recoverable.

hpc.4*:

• Redundant power
• CPU: Intel(R) Xeon(R) CPU Gold 6148, with hyper-threading
• Network: Redundant 25 Gb/s
• Flavor disk: Stored in the central storage
• Single-node or disk failures may cause downtime, but instances are recoverable.

I/O flavors

Typical use cases:

• Hadoop/Spark
• Non-critical centralized databases
• Clustered databases

I/O flavors are intended to provide the best I/O performance on the virtual machine root and ephemeral disks.

As these instances are also tightly tied to the hardware, you may expect downtime of instances during the maintenance of the hardware.

The bulk of the storage is available as an ephemeral disk, typically in /dev/vdb.

Often you want to create clusters of servers with the io.* flavors. In these cases, you probably want to have your virtual machines land on different physical servers. This cannot currently be done in the web interface. To do this, please refer to the anti-affinity group commands in our command line instructions.

The availability of these instances is not as high as the standard flavors, but the I/O performance is significantly better.

cPouta IO flavor characteristics:

io.70GB-700GB:

Note

These servers have non-redundant disks, and you may expect data loss in case of disk failure.
These virtual machines can not be migrated nor resized to a different family flavor.

• Redundant power
• CPU: Intel(R) Xeon(R) CPU E5-2680 v3, with hyper-threading
• Network: Redundant 10 Gb/s or 40 Gb/s
• Flavor disk: Local SSD disks, RAID-0
• Instances can be lost due to a single-node or disk failure.

io.2.*:

Note

These virtual machines can not be resized to a different family flavor.

• Redundant power
• CPU: AMD EPYC 7282 16-Core Processor
• Network: Redundant 25 Gb/s
• Flavor disk: Local NVMe disk, RAID-1
• Instance can be lost due to a single-node or multiple simultaneous disk failures.

ePouta IO flavor characteristics:

io.2.*:

Note

These virtual machines can not be resized to a different family flavor.

• Redundant power
• CPU: AMD EPYC 7313 16-Core Processor
• Network: Redundant 25 Gb/s
• Flavor disk: Local NVMe disk, RAID-1
• Instance can be lost due to a single-node or multiple simultaneous disk failures.

GPU flavors

Typical use cases:

• High performance compute applications leveraging GPUs
• Machine and deep learning, e.g. TensorFlow
• Rendering

The GPU flavors are intended to provide high performance computing using GPGPU (General Purpose computing on Graphical Processing Units). GPGPUs can significantly speed up certain algorithms and applications. The GPGPUs are suitable for deep learning, scientific computing as well as for remote desktops, rendering or visualization.

The GPGPU flavors are backed by local SSD on the servers. The SSDs in gpu.1 flavors are configured in RAID-1. This is where the OS root disk is stored. With gpu.2 flavors, the SSDs are bigger and the SSDs are configured in RAID-0 for faster staging of datasets. You can use the volumes for storing larger data sets and persistent data. If you need to read and write a lot of data between the disk and GPGPU, using volumes might affect performance when compared to local SSD disk.

To take advantage of the acceleration which GPGPUs provide, the applications you run must support them. If you write your own applications, the optimization service helps in leveraging the GPGPUs.

GPGPUs can be used for a lot of cool and interesting things, but please remember the resource usage must comply with the Terms of Use.

Limitations and caveats: 

• As we use PCI passthrough to get the whole GPGPU into the instance. The administrators are not able to access the GPGPU and check its health. Please report any errors or problems with the GPGPUs to CSC (and attach the output of the command "nvidia-smi -q").
• The applications must be able to utilize the GPU to get a speedup. Even though there is no specific speedup target to be met to enable GPU usage on Pouta, it is best to aim for higher speedups to compensate for the relatively higher prices per hour associated with GPUs and their relative scarcity.
• As the majority of computing resources in Pouta are CPU-based and GPU resources are relatively limited, most likely, you will need to specify your need for GPU resources in your application or make an additional request via servicedesk@csc.fi to enable their usage on your existing Pouta application.

These instances are also tightly tied to the hardware. You may expect downtime of instances during the maintenance of the hardware.

Users also have the possibility to use NVIDIA Volta V100 GPGPUs in the batch system Puhti.

cPouta flavor characteristics:

gpu.1.*:

• GPU: NVIDIA Tesla P100 (16 GB)
• CPU: Intel(R) Xeon(R) CPU E5-2680 v4, with hyper-threading
• Network: Redundant 10 Gb/s
• Flavor disk: Local SSD disks, RAID-1
• No redundant power
• Instance can be lost due to a single-node or multiple simultaneous disk failures.

ePouta flavor characteristics:

gpu.1.*:

• GPU: NVIDIA Tesla P100 (16 GB)
• CPU: Intel(R) Xeon(R) CPU E5-2680 v4, with hyper-threading
• Network: Redundant 10 Gb/s
• Flavor disk: Local SSD disks, RAID-1
• Redundant power
• Instance can be lost due to a single-node or disk failure.

gpu.2.*:

• GPU: NVIDIA Tesla V100 (16 GB)
• CPU: Intel(R) Xeon(R) Gold 6148, with hyper-threading
• NUMA Aware: yes (CPU <> memory, not PCI devices)
• Network: Redundant 10 Gb/s
• Flavor disk: Local SSD disks, RAID-0
• Redundant power
• Instance can be lost due to a single-node or disk failure.

gpu.3.*:

• GPU: NVIDIA A100 (40 GB)
• CPU: AMD EPYC 7402 24-Core Processor
• Network: Redundant 10 Gb/s
• Flavor disk: Local NVMe disks
• Redundant power
• Instance can be lost due to a single-node or disk failure.
• Multi-Instance GPU (MIG) functionality supported

High memory flavors (only in ePouta)

Typical use cases:

• Scientific applications requiring large amounts of memory

These flavors have large amounts of memory and are meant for use cases which require and can utilize such amounts of memory. Typical use cases of these flavors include genome sequencing and analysis applications.

The resize/migration functionalities do not work for these instances.

If you need to move a workload from another type of VM to an instance with a high memory flavor, i.e., a TB instance, either move all data and install all applications manually on the new TB instance or create a snapshot of the source VM. Then convert that snapshot to a volume and use the volume to create the new TB-flavor VM.

If you need to move a workload from a TB instance to another instance, either move all data and install all applications manually on a new VM or create a snapshot of the source VM. Please note that all ephemeral disk data will be lost in the process and will not be stored in the snapshot because only the TB VM root disk is stored in the snapshot.

Flavor characteristics:

tb.3.*:

• Redundant power
• CPU: Intel(R) Xeon(R) CPU E5-2680 v4, with hyper-threading or Intel(R) Xeon(R) CPU E5-2698 v4, with hyper-threading
• Network: Redundant 25 Gb/s
• Flavor disk: Local SSD disks, RAID-0
• Instances can be lost due to a single-node or disk failure.

Deprecated flavors

This is the set of original flavors that has been available since the launch. You should not launch any new virtual machines using any of these flavors. Existing virtual machines that use these flavors will continue to work. We will maintain these flavors for a period of time, but they will be removed at some point in the near future.

Flavor	Cores	Memory (GiB)	Root disk (GB)	Ephemeral disk (GB)	Total disk (GB)	Memory/ core (GiB)	Redundancy	Billing Units /h
hpc-gen1.1core	1	3.7	80 (RAID0)	0	80	3.7		2
hpc-gen1.4core	4	15	80 (RAID0)	0	80	3.7		8
hpc-gen1.8core	8	30	80 (RAID0)	0	80	3.7		16
hpc-gen1.16core	16	60	80 (RAID0)	0	80	3.7		32
hpc-gen2.2core	2	10	80 (RAID0)	0	80	5		4
hpc-gen2.8core	8	40	80 (RAID0)	0	80	5		15
hpc-gen2.16core	16	80	80 (RAID0)	0	80	5		30
hpc-gen2.24core	24	117	80	0	80	4.8		30
hpc-gen2.48core	48	234	80	0	80	4.8		60
tiny	1	1	10 (RAID0)	110 (RAID0)	120	1		2
mini	1	3.5	10 (RAID0)	110 (RAID0)	120	1.7		2
small	4	15	10 (RAID0)	220 (RAID0)	230	3.8		8
medium	8	30	10 (RAID0)	440 (RAID0)	450	3.8		16
large	12	45	10 (RAID0)	660 (RAID0)	670	3.8		24
fullnode	16	60	10 (RAID0)	900 (RAID0)	910	3.8		32
hpc.mini	2	3.5	80	0	80	1.8		5
hpc.small	4	7	80	0	80	1.8		10
hpc.medium.haswell	8	40	80	0	80	5		20
hpc.large.haswell	16	80	80	0	80	5		40
hpc.xlarge.haswell	32	156	80	0	80	5		80
hpc.fullnode.haswell	46	242	80	0	80	5.2		72
hpc.medium.westmere	8	14	80	0	80	1.8		8
hpc.large.westmere	16	28	80	0	80	1.8		16
hpc.xlarge.westmere	23	41	80	0	80	1.8		24
hpc.largemem.westmere	23	90	80	0	80	4		36
hpc.3.28core	28	120	80	0	80	4.2		48
hpc.3.56core	56	240	80	0	80	4.2		96
io.haswell.2core	2	9.7	20	70	90	4.8		4.5
io.haswell.4core	4	19	20	160	180	4.7		9
io.haswell.8core	8	39	20	350	370	4.8		18
io.haswell.16core	16	78	20	700	720	4.8		36
io.haswell.32core	32	156	20	1400	1420	4.8		72
io.haswell.46core	46	242	20	2100	2120	5.2		108
tb.4.735RAM	80	735	80 (SSD/RAID0)	3300 (SSD/RAID0)	3380	9.2		220 (350)
tb.westmere.32core	32	488	80 (RAID6)	3250 (RAID6)	3330	15.2		200
tb.westmere.64core	64	976	80 (RAID6)	6500 (RAID6)	6580	15.2		400

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