M2 family · Memory Optimizedx86_64 · Intel/AMD

m2-hypermem-416

The m2-hypermem-416 machine type has 416 vCPUs and 8832 GB of memory. Pricing for this instance starts at $67.26 per hour and $49098.05 monthly in the us-central1 region.

Updated June 16, 2026
vCPUs
416
Memory
8832 GB
Network
Up to 32 Gbps
Storage
Persistent Disk

Save up to $0/mo on every m2-hypermem-416

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
$67.2576$49098.05 Baseline
1-Year Committed Use Discount (CUD)
$40.6378$29665.56 -40%
3-Year Committed Use Discount (CUD)
$23.2765$16991.83 -65%
Preemptible / Spot
$67.2576$49098.05 Baseline
You could save $0.00/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
$49098.05
730 hrs × $67.2576/hr × 1

Compute

x86_64 · Intel/AMD
vCPUs i416
Memory i8832 GB
Physical processorIntel Cascade Lake
Nested VirtualizationNot supported
Sole TenantNot supported
GPUNone
Within M2 family
m2-ultramem-208
208 vCPU
m2-megamem-416
416 vCPU
m2-hypermem-416
416 vCPU
m2-ultramem-416
416 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 bandwidthN/A
Enhanced networking iYes (gvnic/virtio)
IPv6 supportYes (Dual-stack VPC)

Storage

Max persistent disks128
Max disk size512 TB
Local SSDsNo local SSD

Compare with another instance

vs.
Specm2-hypermem-416m2-ultramem-208Δ
vCPUs416208-50%
Memory8832 GB5888 GB-33%
Hourly Price$67.2576$42.1119-37%
Monthly Price$49098.05$30741.71-37%
Cost optimization

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

Connect your GCP account. We'll identify every underutilized m2.* instance and show exactly where downsizing, cleaning up orphaned disks, or shifting to CUDs saves money — with zero code changes.