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Yandex Data Processing
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  1. Concepts
  2. Host classes
  3. Active host classes

Yandex Data Processing host classes

Written by
Yandex Cloud
Updated at November 26, 2025

The host class determines the computing power allocated for each host in a cluster. When you change the host class for a cluster, all existing hosts change accordingly.

The available storage size does not depend on the host class. For storage limitations, see Quotas and limits.

Available host classesAvailable host classes

Hosts in Yandex Data Processing clusters are deployed on Yandex Compute Cloud VMs. You can create these VMs on any platforms Compute Cloud supports. To learn more about the platforms, see Platforms.

A suitable host class depends on driver deploy mode:

  • In deployMode=cluster mode, when the driver is deployed on one of the cluster's compute hosts, 4-8 CPU cores and 16 GB RAM are sufficient for the subcluster with the master host.
  • In deployMode=client mode, when the driver is deployed on the cluster's master host, the computing power depends on the job logic and the number of concurrent jobs.

For more information about driver deploy modes and computing resource consumption, see Resource allocation.

The full list of possible host configurations on each platform is provided below.

Note

The Intel Broadwell platform is not available for clusters with hosts residing in the ru-central1-d availability zone.

Alert

Starting January 1, 2024, the following existing host classes are deprecated: b1.nano, b1.micro, b1.small, b2.nano, b2.micro, and b2.small. New hosts of these classes cannot be created since June 20, 2023.

Host class name Number of CPUs CPU performance RAM, GB Disk
size, GB
Intel Broadwell
g1.small 8 100% 96 20 — 8,184
m1.micro 2 100% 16 20 — 8,184
m1.small 4 100% 32 20 — 8,184
m1.medium 6 100% 48 20 — 8,184
m1.large 8 100% 64 20 — 8,184
m1.xlarge 12 100% 96 20 — 8,184
m1.2xlarge 16 100% 128 20 — 8,184
m1.3xlarge 24 100% 192 20 — 8,184
m1.4xlarge 32 100% 256 20 — 8,184
s1.nano 1 100% 4 20 — 8,184
s1.micro 2 100% 8 20 — 8,184
s1.small 4 100% 16 20 — 8,184
s1.medium 8 100% 32 20 — 8,184
s1.large 16 100% 64 20 — 8,184
s1.xlarge 32 100% 128 20 — 8,184
Intel Cascade Lake
b2.medium 2 50% 4 20 — 8,184
m2.micro 2 100% 16 20 — 8,184
m2.small 4 100% 32 20 — 8,184
m2.medium 6 100% 48 20 — 8,184
m2.large 8 100% 64 20 — 8,184
m2.xlarge 12 100% 96 20 — 8,184
m2.2xlarge 16 100% 128 20 — 8,184
m2.3xlarge 24 100% 192 20 — 8,184
m2.4xlarge 32 100% 256 20 — 8,184
m2.5xlarge 40 100% 320 20 — 8,184
m2.6xlarge 48 100% 384 20 — 8,184
m2.7xlarge 56 100% 448 20 — 8,184
m2.8xlarge 64 100% 512 20 — 8,184
s2.micro 2 100% 8 20 — 8,184
s2.small 4 100% 16 20 — 8,184
s2.medium 8 100% 32 20 — 8,184
s2.large 12 100% 48 20 — 8,184
s2.xlarge 16 100% 64 20 — 8,184
s2.2xlarge 24 100% 96 20 — 8,184
s2.3xlarge 32 100% 128 20 — 8,184
s2.4xlarge 40 100% 160 20 — 8,184
s2.5xlarge 48 100% 192 20 — 8,184
s2.6xlarge 64 100% 256 20 — 8,184
Intel Ice Lake
b3-c1-m4 2 50% 4 20 — 8,184
c3-c2-m4 2 100% 4 20 — 8,184
c3-c4-m8 4 100% 8 20 — 8,184
c3-c8-m16 8 100% 16 20 — 8,184
c3-c12-m24 12 100% 24 20 — 8,184
c3-c16-m32 16 100% 32 20 — 8,184
c3-c24-m48 24 100% 48 20 — 8,184
c3-c32-m64 32 100% 64 20 — 8,184
c3-c40-m80 40 100% 80 20 — 8,184
c3-c48-m96 48 100% 96 20 — 8,184
c3-c64-m128 64 100% 128 20 — 8,184
c3-c80-m160 80 100% 160 20 — 8,184
c3-c96-m192 96 100% 192 20 — 8,184
s3-c2-m8 2 100% 8 20 — 8,184
s3-c4-m16 4 100% 16 20 — 8,184
s3-c8-m32 8 100% 32 20 — 8,184
s3-c12-m48 12 100% 48 20 — 8,184
s3-c16-m64 16 100% 64 20 — 8,184
s3-c24-m96 24 100% 96 20 — 8,184
s3-c32-m128 32 100% 128 20 — 8,184
s3-c40-m160 40 100% 160 20 — 8,184
s3-c48-m192 48 100% 192 20 — 8,184
s3-c64-m256 64 100% 256 20 — 8,184
s3-c80-m320 80 100% 320 20 — 8,184
s3-c96-m576 96 100% 576 20 — 8,184
m3-c2-m16 2 100% 16 20 — 8,184
m3-c4-m32 4 100% 32 20 — 8,184
m3-c6-m48 6 100% 48 20 — 8,184
m3-c8-m64 8 100% 64 20 — 8,184
m3-c12-m96 12 100% 96 20 — 8,184
m3-c16-m128 16 100% 128 20 — 8,184
m3-c24-m192 24 100% 192 20 — 8,184
m3-c32-m256 32 100% 256 20 — 8,184
m3-c40-m320 40 100% 320 20 — 8,184
m3-c48-m384 48 100% 384 20 — 8,184
m3-c56-m448 56 100% 448 20 — 8,184
m3-c64-m512 64 100% 512 20 — 8,184
m3-c80-m640 80 100% 640 20 — 8,184
AMD Zen 4
c4a-c2-m4 2 100% 4 20 — 8,184
c4a-c4-m8 4 100% 8 20 — 8,184
c4a-c8-m16 8 100% 16 20 — 8,184
c4a-c16-m32 16 100% 32 20 — 8,184
c4a-c32-m64 32 100% 64 20 — 8,184
c4a-c64-m128 64 100% 128 20 — 8,184
c4a-c96-m192 96 100% 192 20 — 8,184
c4a-c128-m256 128 100% 256 20 — 8,184
c4a-c224-m448 224 100% 448 20 — 8,184
c4a-c256-m512 256 100% 512 20 — 8,184
s4a-c2-m8 2 100% 8 20 — 8,184
s4a-c4-m16 4 100% 16 20 — 8,184
s4a-c8-m32 8 100% 32 20 — 8,184
s4a-c16-m64 16 100% 64 20 — 8,184
s4a-c32-m128 32 100% 128 20 — 8,184
s4a-c64-m256 64 100% 256 20 — 8,184
s4a-c96-m384 96 100% 384 20 — 8,184
s4a-c128-m512 128 100% 512 20 — 8,184
s4a-c224-m896 224 100% 896 20 — 8,184
s4a-c256-m1024 256 100% 1,024 20 — 8,184
m4a-c2-m16 2 100% 16 20 — 8,184
m4a-c4-m32 4 100% 32 20 — 8,184
m4a-c8-m64 8 100% 64 20 — 8,184
m4a-c16-m128 16 100% 128 20 — 8,184
m4a-c32-m256 32 100% 256 20 — 8,184
m4a-c64-m512 64 100% 512 20 — 8,184
m4a-c96-m768 96 100% 768 20 — 8,184
m4a-c128-m1024 128 100% 1,024 20 — 8,184
m4a-c224-m1792 224 100% 1,792 20 — 8,184

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