Instances›AWS EC2›g4dn›g4dn.16xlarge
Compute
vCPUs64
Memory256 GiB
Mem / vCPU4.00 GiB
ProcessorXeon Family
Clock speed2.5 GHz
Architecturex86_64
Storage
Type1 × 900 GB NVMe SSD
Bandwidth9,500 Mbps
Throughput1187.5 MB/s
IOPS40,000
Networking
Performance50 Gigabit
Max ENIs4
IPv6Yes
GPUNVIDIA T4 Tensor Core
Family sizes
g4dn.xlarge4 vCPU · 16 GiB
g4dn.2xlarge8 vCPU · 32 GiB
g4dn.4xlarge16 vCPU · 64 GiB
g4dn.8xlarge32 vCPU · 128 GiB
g4dn.12xlarge48 vCPU · 192 GiB
g4dn.16xlarge64 vCPU · 256 GiB
g4dn.metal96 vCPU · 384 GiB
g4dn.16xlarge

g4dn.16xlarge is AWS's GPU EC2 size at 64 vCPU and 256 GiB. In the name, "g" is graphics, "4" is the generation, "dn" marks local NVMe storage and enhanced networking and "16xlarge" is the size within the g4dn family. It runs on Intel Xeon Family at 2.5 GHz, and AWS introduced it in September 2019. AWS has since shipped g5, g6 and g7; g5.16xlarge is the direct replacement at this size, scoring 1,246,915 CoreMark/s against this instance's 914,808 — roughly 36% more throughput for the same vCPU count. Rackspace Spot runs one GPU class, gpu.vs2.xlargeplusplus — a single NVIDIA A30 with 24 vCPU and 64 GiB, in San Jose only. Against this instance's 1 × NVIDIA T4 Tensor Core that is a fair swap for inference and media work — the A30 carries more memory and more throughput per card — and it prices $5.22/hr on AWS in US West (N. California), from $0.191/hr on Rackspace Spot.

Match qualityComparable GPU

g4dn.16xlarge pairs 1 Turing GPU (16 GiB VRAM) with 64 vCPU. Spot's GPU node uses the NVIDIA A30 (Ampere, 24 GB VRAM) with 24 vCPU and 64 GiB of memory — newer hardware with more GPU memory, but 24 vCPU against 64. For inference, batch transcoding and single-card fine-tuning the card is the upgrade; anything CPU-bound running alongside it gets less to work with. The catch is location rather than hardware: US West, San Jose is the only Spot region that offers it, so latency to your users and egress to your data are the things to check.

No local diskg4dn.16xlarge comes with 1 × 900 GB NVMe SSD attached to the machine. Spot nodes use network storage only, so that disk doesn't come with it. If you were using it as fast scratch space, you'd move to a persistent volume or object storage, which are slower to read.
Pricing by region
1 hr
RegionAWS on-demandAWS Spot1-yr Reserved3-yr ReservedRackspace SpotYou save
US West, San Jose$5.222$0.582$3.674$2.538$0.191
$5.031 savings
When to use it
Good for
  • ●Video transcoding and image processing
  • ●Machine learning experimentation and fine-tuning
  • ●Remote graphics workstations and rendering
  • ●Workloads that fit inside 256 GiB of RAM
  • ●Jobs that want 1 × 900 GB of local NVMe scratch space
  • ●Network-heavy services — AWS rates this size at 50 Gigabit
Avoid if
  • ●Starting a new deployment. g5.16xlarge is the newer generation at this size.
  • ●The working set needs more than 256 GiB of RAM
  • ●Anything the CPU can do fast enough. GPU hours are the most expensive on the price list.
  • ●More than 4.00 GiB of RAM per vCPU is needed — that ratio is fixed for the whole family
  • ●The work is single-threaded. Most of these 64 vCPU would sit idle.
$3,812/mo on AWS on-demand against $139/mo on Rackspace Spot for the equivalent — market-priced, not list-priced.
  • 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.
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