Virtual Machine Resource Profiles

A resource profile defines the compute capacity allocated to a VM, including vCPUs, memory, and optional GPU resources. Choose a resource profile based on your workload's performance requirements and characteristics.

Profile types

The evroc compute service offers three types of CPU-based profiles:

  • General-purpose (a1a) - Balanced CPU and memory ratio (1:4 - in units of number of vCPUs to memory in GB), suitable for most workloads including web servers, development environments, and small databases
  • Compute-optimized (c1a) - Higher CPU-to-memory ratio (1:2), ideal for compute-intensive applications like batch processing, scientific computing, and high-traffic web servers
  • Memory-optimized (m1a) - Higher memory-to-CPU ratio (1:8), designed for memory-intensive workloads like in-memory databases, caching servers, and data analytics

Available CPU profiles

Profile NamevCPUsMemoryArchitecture
a1a.xs14 GBamd64
a1a.s28 GBamd64
a1a.m416 GBamd64
a1a.l832 GBamd64
a1a.xl1664 GBamd64
a1a.2xl32128 GBamd64
c1a.s24 GBamd64
c1a.m48 GBamd64
c1a.l816 GBamd64
c1a.xl1632 GBamd64
c1a.2xl3264 GBamd64
m1a.s216 GBamd64
m1a.m432 GBamd64
m1a.l864 GBamd64
m1a.xl16128 GBamd64

GPU profiles

GPU-equipped VMs are designed for machine learning training and inference, AI workloads, and high-performance computing. GPU VMs include local NVMe SSD storage for high-throughput data access.

Profile NamevCPUsMemoryArchitectureGPU modelGPU quantityLocal disk
gn-l40s.s15198 GBamd64NVIDIA L40S13,800 GB
gn-l40s.m30396 GBamd64NVIDIA L40S27,600 GB
gn-l40s.l60792 GBamd64NVIDIA L40S415,200 GB
gn-b200.s26262 GBamd64NVIDIA B20014 TB
gn-b200.m52524 GBamd64NVIDIA B20028 TB
gn-b200.l1041048 GBamd64NVIDIA B200416 TB
gn-b200.xl2082096 GBamd64NVIDIA B200832 TB

Deprecated profiles

The following profiles use the previous naming scheme and are deprecated. Existing VMs using these profiles continue to work, but new deployments should use the current naming scheme (a1a, c1a, m1a) shown above:

Profile NamevCPUsMemoryArchitecture
general.xs14 GBamd64
general.s28 GBamd64
general.m416 GBamd64
general.l832 GBamd64
general.xl1664 GBamd64
general.xxl32128 GBamd64
compute-optimized.s24 GBamd64
compute-optimized.m48 GBamd64
compute-optimized.l816 GBamd64
compute-optimized.xl1632 GBamd64
compute-optimized.xxl3264 GBamd64
memory-optimized.s216 GBamd64
memory-optimized.m432 GBamd64
memory-optimized.l864 GBamd64
memory-optimized.xl16128 GBamd64

Choosing a profile

Consider these factors when selecting a resource profile:

  • Workload type - Match CPU, memory, and GPU requirements to your application's needs
  • Performance requirements - Start with a smaller profile and scale up based on actual usage
  • Cost - Larger profiles cost more; right-size your VMs to avoid over-provisioning

You can stop a VM and resize it to a different profile if your requirements change.

Next steps