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On this page
  • About Instance Types
  • Availability
  • GPU Instance Types
  • FPGA Instance Types
  • OS Support
  • Resource Usage
  • New Versions of Existing Instance Types
  • Instance Type Names
  • GPU and FPGA Instance Type Names
  • Available Instance Types
  • Standard AWS Instance Types
  • Standard Azure Instance Types
  • GPU Instance Types
  • FPGA Instance Types

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Instance Types

Learn about the full range of AWS and Azure instance types available on the DNAnexus Platform.

Last updated 2 months ago

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About Instance Types

Availability

This page provides a complete list of the AWS and Azure instance types that are available for use on the DNAnexus Platform.

Not all instance types are accessible to all customers. If you want to use an instance type listed here, but cannot access it when running an analysis, .

A list of instance types to which you have access is available via the command-line interface (CLI), by entering the command dx run --instance-type-help. When using the user interface (UI) to configure a tool to run an analysis, you can see this list in the .

GPU Instance Types

AWS and Azure GPU instance types are available for use on the Platform. See the and learn .

FPGA Instance Types

AWS FPGA instance types are available for use on the Platform. See the and .

OS Support

Ubuntu Linux 24.04 and 20.04 are supported on all instance types in all regions.

Resource Usage

When using any instance, note that some of the resources on that instance will be used by the Platform, to run processes that support your job and provide API services. Around 5% of the available storage, for example, will be used by the Platform; your job's virtual file system will be able to use the remainder as local scratch space. Some amount of available memory will also be used by the Platform.

New Versions of Existing Instance Types

DNAnexus regularly adds new versions of existing instance types, as AWS and Azure make them available. These new versions feature better hardware, such as a more powerful CPU, more memory, more local storage, or some combination of these elements.

Instance Type Names

Cloud provider prefix

Memory infix

Storage infix

Version infix

Core suffix

∅

(AWS)

azure:

(Azure)

+

mem1_

(<=2GB/core)

mem2_

(~4GB/core)

mem3_

(>=7GB/core)

mem4_

(~14GB/core)

mem5_

(~28GB/core)

+

ssd1_

(<=20GB/core)

ssd2_

(~32GB-128GB/core)

ssd3_

(>600GB/core)

hdd2_

(>100GB/core)

+

∅

(version1)

v2_

(version2)

+

x1

x2

x4

x8

x16

x20

x32

x36

x40

x48

x64

x96

x128

Cloud provider prefix: Denotes the cloud provider.

Memory infix: Denotes the memory capacity (per core).

Storage infix: Denotes the local storage technology and capacity (per core). ssd represents a solid-state drive, whereas hdd represents a regular hard disk drive.

Version infix (optional): Denotes the version of the instance type.

Core suffix: Denotes the number of cores.

For example, mem1_ssd1_v2_x8 is the second version of an instance type available on AWS, featuring 8 cores, 16GB of memory (2GB/core), and 160GB of solid-state drive storage (20GB/core). Similarly, azure:mem1_ssd1_x8 is an instance type available on Azure, featuring 8 cores, 15.7GB of memory (~1.9GB/core), and 128GB of solid-state drive storage (16GB/core).

GPU and FPGA Instance Type Names

GPU and FPGA instance type names following the schema described above, but with the inclusion of an additional infix. This infix is included before the version suffix, and provides information on the instance type (GPU or FPGA). Many such infixes also include a number, indicating the number of GPUs or FPGA included in the instance.

For example, the AWS FPGA instance typemem3_ssd2_fpga1_x8 includes 1 FPGA. The AWS GPU instance type mem2_ssd1_gpu4_x48, meanwhile, includes 4 GPUs.

Available Instance Types

Standard AWS Instance Types

Instance Type

Cores

Memory (GB)

Storage (GB)

mem1_hdd1_x2

2

3.75

200

mem1_hdd1_x4

4

7.5

400

mem1_hdd1_x8

8

15

800

mem1_hdd1_x16

16

30

1600

mem1_hdd1_x36

36

60

3200

mem1_hdd1_v2_x2

2

4

200

mem1_hdd1_v2_x4

4

8

400

mem1_hdd1_v2_x8

8

16

800

mem1_hdd1_v2_x16

16

32

1600

mem1_hdd1_v2_x36

36

72

3600

mem1_hdd1_v2_x72

72

144

7200

mem1_hdd1_v2_x96

96

192

9600

mem1_ssd1_x2

2

3.8

40

mem1_ssd1_x4

4

7.5

80

mem1_ssd1_x8

8

15

160

mem1_ssd1_x16

16

30

320

mem1_ssd1_x32

32

60

640

mem1_ssd1_x36

36

72

900

mem1_ssd1_v2_x2

2

4

50

mem1_ssd1_v2_x4

4

8

100

mem1_ssd1_v2_x8

8

16

200

mem1_ssd1_v2_x16

16

32

400

mem1_ssd1_v2_x36

36

72

900

mem1_ssd1_v2_x72

72

144

1,800

mem1_ssd2_x2

2

3.8

160

mem1_ssd2_x4

4

7.5

320

mem1_ssd2_x8

8

15

640

mem1_ssd2_x16

16

30

1,280

mem1_ssd2_x36

36

60

2,880

mem1_ssd2_v2_x2

2

4

160

mem1_ssd2_v2_x4

4

8

320

mem1_ssd2_v2_x8

8

16

640

mem1_ssd2_v2_x16

16

32

1,280

mem1_ssd2_v2_x36

36

72

2,880

mem1_ssd2_v2_x72

72

144

5,760

mem1_hdd2_x1

1

1.7

160

mem1_hdd2_x8

8

7

1,680

mem1_hdd2_x32

32

60.5

3,360

mem2_ssd1_x2

2

7.5

40

mem2_ssd1_x4

4

15

80

mem2_ssd1_x8

8

30

160

mem2_ssd1_v2_x2

2

8

75

mem2_ssd1_v2_x4

4

16

150

mem2_ssd1_v2_x8

8

32

300

mem2_ssd1_v2_x16

16

64

600

mem2_ssd1_v2_x32

32

128

1,200

mem2_ssd1_v2_x48

48

192

1,800

mem2_ssd1_v2_x64

64

256

2,400

mem2_ssd1_v2_x96

96

384

3,600

mem2_ssd2_x2

2

8

160

mem2_ssd2_x4

4

16

320

mem2_ssd2_x8

8

32

1280

mem2_ssd2_x16

16

64

2560

mem2_ssd2_x40

40

160

3200

mem2_ssd2_x64

64

256

5120

mem2_ssd2_v2_x2

2

8

160

mem2_ssd2_v2_x4

4

16

320

mem2_ssd2_v2_x8

8

32

640

mem2_ssd2_v2_x16

16

64

1280

mem2_ssd2_v2_x32

32

128

2560

mem2_ssd2_v2_x48

48

192

3840

mem2_ssd2_v2_x64

64

256

5120

mem2_ssd2_v2_x96

96

384

7480

mem2_hdd2_x1

1

3.8

410

mem2_hdd2_x2

2

7.5

840

mem2_hdd2_x4

4

15

1,680

mem2_hdd2_v2_x2

2

8

1,000

mem2_hdd2_v2_x4

4

16

2,000

mem3_ssd1_x2

2

15

40

mem3_ssd1_x4

4

30.5

80

mem3_ssd1_x8

8

61

160

mem3_ssd1_x16

16

122

320

mem3_ssd1_x32

32

244

640

mem3_ssd1_v2_x2

2

16

75

mem3_ssd1_v2_x4

4

32

150

mem3_ssd1_v2_x8

8

64

300

mem3_ssd1_v2_x16

16

128

600

mem3_ssd1_v2_x32

32

256

1,200

mem3_ssd1_v2_x48

48

384

1,800

mem3_ssd1_v2_x64

64

512

3,200

mem3_ssd1_v2_x96

96

768

3,600

mem3_ssd2_x4

4

30.5

800

mem3_ssd2_x8

8

61

1,600

mem3_ssd2_x16

16

122

3,200

mem3_ssd2_x32

32

244

6,400

mem3_ssd2_v2_x2

2

15.25

475

mem3_ssd2_v2_x4

4

30.5

950

mem3_ssd2_v2_x8

8

61

1,900

mem3_ssd2_v2_x16

16

122

3,800

mem3_ssd2_v2_x32

32

244

7,600

mem3_ssd2_v2_x64

64

488

15,200

mem3_ssd3_x2

2

16

1,250

mem3_ssd3_x4

4

32

2,500

mem3_ssd3_x8

8

64

5,000

mem3_ssd3_x12

12

96

7,500

mem3_ssd3_x24

24

192

15,000

mem3_ssd3_x48

48

384

30,000

mem3_ssd3_x96

96

768

60,000

mem3_hdd2_x2

2

17.1

420

mem3_hdd2_x4

4

34.2

850

mem3_hdd2_x8

8

68.4

1,680

mem3_hdd2_v2_x2

2

16

500

mem3_hdd2_v2_x4

4

32

1,000

mem3_hdd2_v2_x8

8

64

2,000

mem4_ssd1_x128

128

1,952

3,840

Standard Azure Instance Types

Instance Type

Cores

Memory (GB)

Storage (GB)

azure:mem1_ssd1_x2

2

3.9

32

azure:mem1_ssd1_x4

4

7.8

64

azure:mem1_ssd1_x8

8

15.7

128

azure:mem1_ssd1_x16

16

31.4

254

azure:mem2_ssd1_x1

1

3.5

128

azure:mem2_ssd1_x2

2

7

128

azure:mem2_ssd1_x4

4

14

128

azure:mem2_ssd1_x8

8

28

256

azure:mem2_ssd1_x16

16

56

512

azure:mem3_ssd1_x2

2

14

128

azure:mem3_ssd1_x4

4

28

128

azure:mem3_ssd1_x8

8

56

256

azure:mem3_ssd1_x16

16

112

512

azure:mem3_ssd1_x20

20

140

640

azure:mem4_ssd1_x2

2

28

128

azure:mem4_ssd1_x4

4

56

128

azure:mem4_ssd1_x8

8

112

256

azure:mem4_ssd1_x16

16

224

512

azure:mem4_ssd1_x32

32

448

1024

azure:mem5_ssd2_x64*

64

1,792

8,192

azure:mem5_ssd2_x128*

128

3,892

16,384

* - These high memory instance types are expensive, use them with caution.

GPU Instance Types

The following table shows all the GPU instance types available on AWS and Azure.

Instance Type

GPU

Cores

Memory (GB)

Storage (GB)

mem1_ssd1_gpu2_x8

1

8

15

60

mem1_ssd1_gpu2_x32

4

32

60

240

mem2_ssd1_gpu_x16

1

16

64

225

mem2_ssd1_gpu_x32

1

32

128

900

mem2_ssd1_gpu_x48

4

48

192

900

mem2_ssd1_gpu_x64

1

64

256

900

mem2_ssd1_gpu1_x32

1

32

128

900

mem2_ssd1_gpu1_x64

1

64

256

900

mem2_ssd1_gpu4_x48

4

48

192

900

mem3_ssd1_gpu_x8

1

8

61

160

mem3_ssd1_gpu_x32 *

4

32

244

640

mem3_ssd1_gpu_x64 *

8

64

488

1,280

azure:mem3_ssd2_gpu4_x64 *

4

64

488

2,048

* - These GPU instance types are costly to use. Use them with caution.

FPGA Instance Types

The following table shows all the FPGA instance types available on AWS.

Instance Type

FPGA

Cores

Memory (GB)

Storage (GB)

mem3_ssd2_fpga1_x8

1

8

122

470

mem3_ssd2_fpga1_x16

1

16

244

940

mem3_ssd2_fpga1_x64

1

64

976

3760

On the Platform, each new instance type version has a version infix, such as _v2, in its name, to distinguish it from the original version of that instance type. See the for more information on version infixes.

While the latest version will deliver better performance, DNAnexus, as a rule, makes both the original and updated version of instance types available. So, for example, you can access both mem1_ssd1_x8 and mem1_ssd1_v2_x8. See the for more information.

With the exception of , instance type names are constructed according to the following scheme:

Not all name combinations are available as instance types. See the section for full detail on available instance types.

For more information, see the detailed lists of and .

Instance Names section
full list of available instance types
GPU and FPGA instance types
Available Instance Types
GPU instances types
FPGA instances types
contact DNAnexus Support
full list below
full list below
Stage Settings pane of the Run Analysis screen
how to specify NVIDIA driver version
learn how to specify FPGA driver version