M3 family · Memory Optimizedx86_64 · Intel/AMD

m3-megamem-128

The m3-megamem-128 machine type has 128 vCPUs and 1952 GB of memory. Pricing for this instance starts at $14.41 per hour and $10519.01 monthly in the us-central1 region.

Updated June 21, 2026
vCPUs
128
Memory
1952 GB
Network
Up to 32 Gbps
Storage
Local SSD

Save up to $5260/mo on every m3-megamem-128

Connect Google Cloud — we'll find the optimal pricing model for your workload.

All pricing options

us-central1 · USD · monthly = 730 hrs
Prices exclude local taxes
TermHourlyMonthlySavings
On-Demand
$14.4096$10519.01 Baseline
1-Year Committed Use Discount (CUD)
$8.5254$6223.57 -41%
3-Year Committed Use Discount (CUD)
$4.3242$3156.64 -70%
Preemptible / Spot
$7.2048$5259.50 -50%
You could save $5259.50/mo on this instance with the right pricing model.
Find my savings

Monthly cost estimator

Estimate your spend based on actual usage.
730 h
1h365h730h (24/7)
1
Estimated monthly
$10519.01
730 hrs × $14.4096/hr × 1

Compute

x86_64 · Intel/AMD
vCPUs i128
Memory i1952 GB
Physical processorIntel Ice Lake
Nested VirtualizationNot supported
Sole TenantNot supported
GPUNone
Within M3 family
m3-ultramem-32
32 vCPU
m3-megamem-64
64 vCPU
m3-ultramem-64
64 vCPU
m3-megamem-128
128 vCPU
m3-ultramem-128
128 vCPU

Lock in this rate across your fleet — typically save 30–40%

Connect your Google Cloud account to apply this pricing logic to every running instance automatically.

Networking

Max egress bandwidth32 Gbps
Tier 1 bandwidth100 Gbps
Enhanced networking iYes (gvnic/virtio)
IPv6 supportYes (Dual-stack VPC)

Storage

Max persistent disks128
Max disk size512 TB
Local SSDsSupported (Scratch Disk)

Compare with another instance

vs.
Specm3-megamem-128m3-ultramem-32Δ
vCPUs12832-75%
Memory1952 GB976 GB-50%
Hourly Price$14.4096$6.0912-58%
Monthly Price$10519.01$4446.58-58%
Cost optimization

GCP recommender alerts can cut this bill ~30-40%.

Connect your GCP account. We'll identify every underutilized m3.* instance and show exactly where downsizing, cleaning up orphaned disks, or shifting to CUDs saves money — with zero code changes.