Instances›AWS EC2›m7g›m7g.16xlarge
Compute
vCPUs64
Memory256 GiB
Mem / vCPU4.00 GiB
ProcessorGraviton3 Processor
Clock speed2.5 GHz
Architecturearm64
Storage
TypeEBS only
Bandwidth20,000 Mbps
Throughput2500 MB/s
IOPS80,000
Networking
Performance30 Gigabit
Max ENIs15
IPv6Yes
GPUNone
Family sizes
m7g.medium1 vCPU · 4 GiB
m7g.large2 vCPU · 8 GiB
m7g.xlarge4 vCPU · 16 GiB
m7g.2xlarge8 vCPU · 32 GiB
m7g.4xlarge16 vCPU · 64 GiB
m7g.8xlarge32 vCPU · 128 GiB
m7g.12xlarge48 vCPU · 192 GiB
m7g.16xlarge64 vCPU · 256 GiB
m7g.metal64 vCPU · 256 GiB
m7g.16xlarge

The m7g.16xlarge is a general-purpose AWS EC2 instance: 64 vCPU and 256 GiB of memory, 4.00 GiB per vCPU. In the name, "m" is the general-purpose line, "7" is the generation, "g" marks AWS Graviton (ARM) processors and "16xlarge" is the size within the m7g family. It runs on AWS Graviton3 Processor at 2.5 GHz, and AWS introduced it in February 2023. AWS has since shipped m8g and m9g; m8g.16xlarge is the direct replacement at this size, scoring 1,941,748 CoreMark/s against this instance's 1,580,052 — roughly 23% more throughput for the same vCPU count. No single Spot node covers 64 vCPU and 256 GiB, so the equivalent is a fleet: 4 × gp.vs2.2xlarge, 64 vCPU and 256 GiB in total, about 94% below the US East (N. Virginia) on-demand rate. Spot delivers compute as managed Kubernetes, so a node pool of that size is the native shape of the answer rather than a workaround — provided the workload scales horizontally.

Match qualityScale-out match

m7g.16xlarge gives you 64 vCPU and 256 GiB in a single machine. The largest comparable Spot node, gp.vs2.2xlarge, is 16 vCPU and 64 GiB, so the equivalent here is 4 nodes rather than one.

Spot runs as managed Kubernetes, so a 4-node pool is the normal way to run things rather than a workaround. If your workload already sits behind a load balancer, a queue or a scheduler, it spreads across them without changes. If it needs all 64 cores and 256 GiB in one process, it does not.

x86_64 onlym7g.16xlarge is Graviton (arm64) and Rackspace Spot has no arm64 nodes, so you'd be moving to x86. You'll need x86 builds of your images, and per-core speed differs between the chips — worth testing before you decide how many nodes to run.
Pricing by region
1 hr
RegionAWS on-demandAWS Spot1-yr Reserved3-yr ReservedRackspace SpotYou save
US East, N. Virginia$2.611$0.821$1.727$1.184$0.164
$2.447 savings
US Central, Chicagono AWS region$0.164
US Central, Dallasno AWS region$0.204
Hong Kong$3.590$2.375$1.629$0.044
$3.546 savings
Sydney, Australia$3.264$1.016$2.159$1.480$0.644
$2.620 savings
London, UK$3.019$0.399$1.994$1.373$0.884
$2.135 savings
US West, San Jose$3.046$0.402$2.009$1.388$0.604
$2.442 savings
US Central, Dallas 2no AWS region$0.004
US East, N. Virginia 2$2.611$0.821$1.727$1.184$0.604
$2.007 savings
When to use it
Good for
  • ●Backend services with no single dominant resource
  • ●Queue workers and scheduled jobs
  • ●Web and application servers with mixed CPU and memory demand
  • ●Workloads that fit inside 256 GiB of RAM
  • ●Parallel work that can occupy 64 vCPU
  • ●Network-heavy services — AWS rates this size at 30 Gigabit
  • ●Anything that shards cleanly across 4 nodes, which is how it maps onto Rackspace Spot
Avoid if
  • ●Starting a new deployment. m8g.16xlarge is the newer generation at this size.
  • ●The working set needs more than 256 GiB of RAM
  • ●You cannot rebuild x86-only binaries or vendor images for aarch64
  • ●Local NVMe or instance-store disk is a hard requirement
  • ●GPU or FPGA acceleration is needed
  • ●Sustained full-core CPU load. A compute-optimized family costs less per unit of work.
m7g.16xlarge runs $1,906/mo on AWS on-demand. The 4-node equivalent on Rackspace Spot: $120/mo.
  • Spot sells spare capacity at market prices. It is the cheapest compute on the internet.
  • Prices are set by supply and demand. You pay what the market decides.
  • Compute is delivered as fully managed Kubernetes. All infrastructure is taken care of.
  • Egress costs $0.01/GB after the free allowance. AWS charges $0.09/GB.
Build your Cloudspace
Frequently asked questions