Virtual machine flavors and billing unit rates

This article lists the types (flavors) of virtual machines and their cost in billing units. Normally billing units are not invoiced for academic use, learn more in here LINKTOBEADDED.

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

Users can create virtual machines with larger compute resources or smaller resources based upon their needs. The Virtual Machine flavors available in cPouta and ePouta are listed below in separate tables.

New prices starting from 18.03.2019. The prices before 18.03.2019 are shown in parentheses.

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)
Disk
(root)
GB
Disk
(ephemeral)
GB
Disk
(total)
GB
Memory/
core
(* GiB)
Billing
Units
/h
standard.tiny 1 1 80 0 80 1 0.25 (0.5)
standard.small 2 2 80 0 80 1 0.5 (1)
standard.medium 3 4 80 0 80 1.3 1 (2)
standard.large 4 8 80 0 80 2 2 (4)
standard.xlarge 6 16 80 0 80 2.6 4 (8)
standard.xxlarge 8 32 80 0 80 4 8 (16)

* Not all memory amounts round exactly to GiB, the closest value has been used. This applies to all tables.

HPC flavors

Flavor Cores Memory
(* GiB)
Disk
(root)
GB
Disk
(ephemeral)
GB
Disk
(total)
GB
Memory/
core
(* GiB)
Billing
Units
/h
hpc.4.5core 5 22 80 0 80 4.3 6 (10)
hpc.4.10core 10 43 80 0 80 4.3 12 (20)
hpc.4.20core 20 86 80 0 80 4.3 25 (40)
hpc.4.40core 40 172 80 0 80 4.3 50 (80)
hpc.4.80core 80 344 80 0 80 4.3 100 (160)
hpc-gen2.24core 24 120 80 (RAID0) 0 80 5 30 (45)
hpc-gen2.48core 48 240 80 (RAID0) 0 80 5 60 (90)

I/O flavors

Flavor Cores Memory
(* GiB)
Disk
(root)
GB
Disk
(ephemeral)
GB
Disk
(total)
GB
Memory/
core
(* GiB)
Billing
Units
/h
io.70GB 2 10 20 (SSD/RAID0) 70 (SSD/RAID0) 90 5 3 (5)
io.160GB 4 20 20 (SSD/RAID0) 160 (SSD/RAID0) 180 5 6 (19)
io.340GB 8 40 20 (SSD/RAID0) 340 (SSD/RAID0) 360 5 12 (20)
io.700GB 16 80 20 (SSD/RAID0) 700 (SSD/RAID0) 720 5 24 (40)

GPU flavors

Flavor Cores GPUs Memory
(* GiB)
Disk (root) GB Disk
(total)
GB
Memory/
core
(* GiB)
Billing
Units
/h
gpu.1.1gpu 14 1 120 80 (SSD/RAID1) 80 8.5 60
gpu.1.2gpu 28 2 240 80 (SSD/RAID1) 80 8.5 120
gpu.1.4gpu 56 4 480 80 (SSD/RAID1) 80 8.5 240

ePouta flavors

The following tables list the available virtual machine flavors in ePouta and their Billing Unit coefficients.

Standard flavors

Flavor Cores Memory
(* GiB)
Disk
(root)
GB
Disk
(ephemeral)
GB
Disk
(total)
GB
Memory/
core
(* GiB)
Billing
Units
/h
standard.tiny 1 1 80 0 80 1 0.25 (0.5)
standard.small 2 2 80 0 80 1 0.5 (1)
standard.medium 3 4 80 0 80 1.3 1 (2)
standard.large 4 8 80 0 80 2 2 (4)
standard.xlarge 6 16 80 0 80 2.6 4 (8)
standard.xxlarge 8 32 80 0 80 4 8 (16)

HPC flavors

Flavor Cores Memory
(* GiB)
Disk
(root)
GB
Disk
(ephemeral)
GB
Disk
(total)
GB
Memory/
core
(* GiB)
Billing
Units
/h
hpc.fullnode.haswell 46 242 80 0 80 5.4 72 (120)
hpc.3.28core 28 120 80 0 80 4.4 48 (70)
hpc.3.56core 56 240 80 0 80 4.4 96 (140)
hpc.4.5core 5 22 80 0 80 4.4 8 (12)
hpc.4.10core 10 45 80 0 80 4.5 15 (23)
hpc.4.20core 20 90 80 0 80 4.4 30 (45)
hpc.4.40core 40 180 80 0 80 4.4 60 (90)
hpc.4.80core 80 360 80 0 80 4.4 120 (180)

I/O flavors

Flavor Cores Memory
(* GiB)
Disk
(root)
GB
Disk
(ephemeral)
GB
Disk
(total)
GB
Memory/
core
(* GiB)
Billing
Units
/h
io.haswell.2core 2 10 20 70 90 5 4.5 (7)
io.haswell.4core 4 20 20 160 180 5 9 (13)
io.haswell.8core 8 40 20 350 370 5 18 (25)
io.haswell.16core 16 80 20 700 720 5 36 (50)
io.haswell.32core 32 160 20 1400 1420 5 72 (100)
io.haswell.46core 46 242 20 2100 2120 5.4 108 (150)

High memory flavors

Flavor Cores Memory
(* GiB)
Disk
(root)
GB
Disk
(ephemeral)
GB
Disk
(total)
GB
Memory/
core
(* GiB)
Billing
Units
/h
tb.3.480RAM 56 480 80 (SSD/RAID0) 1650 (NVMe/RAID0) 1730 8.5 110 (240)
tb.3.1470RAM 80 1470 80 (SSD/RAID0) 2500 (NVMe/RAID0) 2580 18.3 320 (600)
tb.4.735RAM 80 735 80 (SSD/RAID0) 3300 (SSD/RAID0) 3380 9.2 220 (350)

GPU flavors (only available via request to servicedesk)

Flavor Cores Memory
(* GiB)
Disk
(root)
GB
Disk
(ephemeral)
GB
Disk
(total)
GB
Memory/
core
(* GiB)
Billing
Units
/h
gpu.2.1gpu 20 180 80 (SSD/RAID0) 1000 (SSD/RAID0) 1080 9 100 (140)

* Not all memory amounts round exactly to GiB, the closest value has been used.

Please note: The flavors in the two tables are slightly different. This is because different hardware is used in these two clouds. Any storage with a comment in parenthesis such as (SSD/RAID0) means that particular storage is local to the compute node. In ePouta, the HPC root disks and standalone volumes are hosted in the centralized Ceph block storage system.

Which type of flavor should I use?

Standard flavors

Typical use cases:

These are generic flavors that are useful for running regular web services like a web server with a database backend or some other relatively light usage. They provide better availability compared to 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 impacted 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:

HPC flavors

Typical use cases:

If your use case is computationally intensive, you should use one of the HPC flavors. The availability for these instances is not as high as with 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.4.*:

hpc-gen2.*:

ePouta HPC flavor characteristics:

hpc.4*:

hpc.3*:

hpc.*.haswell:

I/O flavors

Typical use cases:

I/O flavors are intetended to give you the best I/O performance on the virtual machine root and ephemeral disks. They are backed by local SSDs on the servers they run on. The SSDs are configured in a RAID-0 configuration for maximal performance. This means there is an increased risk of loss of a virtual machine in case of hardware problems. The risk of disk failure is larger than on the other flavors, so it's especially important to be aware of the risks of data-loss on these flavors.

As these instances are also tightly tied to the hardware, you may expect downtime of instances during maintenance of the hardware. Resize/migration functionality also does not work for these instances. The bulk of the storage is available as an ephemeral disk, normally under /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 can not currently be done in the web interface. To achieve this, please refer to the anti-affinity group commands in our command line instructions.

The availability for these instances is not as high as with the standard flavors, but you get significantly better I/O performance. Maintenance work can cause larger disruption, and the resize functionality does not work.

cPouta IO flavor characteristics:

io.*:

ePouta IO flavor characteristics:

io.haswell.*:

GPU Flavors

Typical Usecases:

GPU flavors are intended to give you high performance computing using GPGPU (General Purpose computing on Graphical Processing Units). GPGPUs can significantly speed up some algorithms and applications. The gpu.1. flavors in cPouta have NVIDIA Tesla P100 GPGPUs. The gpu.2.1gpu in ePouta have NVIDIA Tesla V100 GPGPU

The GPGPUs are suitable for deep learning, scientific computing as well as for remote desktop, rendering or visualization. The GPGPU flavors are backed by local SSD on the servers. The SSD in cPouta are configured in RAID-1 and this is where the OS root disk is stored. In ePouta the SSD are bigger than in cPouta and the SSDs are configured in RAID-0 for faster staging of datasets. You can use the volumes for storing larger data sets. If you need to read and write a lot of data between the disk and GPGPU, this might affect the performance.

To take advantage of the acceleration which GPGPUs provide, the applications you run must have support for using them. If you write your own applications, the Optimization Service can offer help in leveraging the GPGPUs.

We know 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 & caveats: 

These instances are also tightly tied to the hardware, you may expect downtime of instances during maintenance of the hardware. The NVIDIA Tesla V100 GPGPU are also available in the batch system on Taito: https://research.csc.fi/taito-gpu.

cPouta flavor characteristics:

gpu.1.*:

ePouta flavor characteristics:

gpu.2.*:

Installation and configuration of GPU Flavors

We have specific CUDA images available for use with the GPU nodes. These images come pre-installed with the freshest CUDA version. Note that the CUDA images are not configure with auto update. One may use any other images with the GPU flavors, but in this case, you will have to install the required libraries yourself. How CSC customizes images can be found in this article.

High Memory Flavors (only in ePouta)

Typical use cases:

These flavors have large amount of memory, and are meant for use cases which require, and can utilize this amount of memory. Typical usecases of these flavors include scientific applications with huge memory requirements for example Gnome sequencing and analysis applications etc.

Resize/migration functionality does not work for these instances.

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

Flavor characteristics:

tb.3.*:

tb.4.*:

Deprecated flavors

This is the set of original flavors that has been available since 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)
Disk
(root)
GB
Disk
(ephemeral)
GB
Disk
(total)
GB
Memory/
core
(* GiB)
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
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.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
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