Instances›AWS EC2›g7e›g7e.12xlarge
Compute
vCPUs48
Memory512 GiB
Mem / vCPU10.67 GiB
ProcessorXeon Scalable
Clock speed3.2 GHz
Architecturex86_64
Storage
Type1 × 3,800 GB NVMe SSD
Bandwidth25,000 Mbps
Throughput3125 MB/s
IOPS100,000
Networking
Performance400 Gigabit
Max ENIs10
IPv6Yes
GPUNVIDIA RTX PRO 6000 Blackwell
Family sizes
g7e.2xlarge8 vCPU · 64 GiB
g7e.4xlarge16 vCPU · 128 GiB
g7e.8xlarge32 vCPU · 256 GiB
g7e.12xlarge48 vCPU · 512 GiB
g7e.24xlarge96 vCPU · 1024 GiB
g7e.48xlarge192 vCPU · 2048 GiB
g7e.12xlarge

The g7e.12xlarge pairs 48 vCPU with 512 GiB of RAM, the 10.67 GiB-per-vCPU ratio that marks a GPU instance. In the name, "g" is graphics, "7" is the generation, "e" marks extended capacity and "12xlarge" is the size within the g7e family. It runs on Intel Xeon Scalable (Emerald Rapids) at 3.2 GHz, and AWS introduced it in January 2026. g7e is the current generation of the g family, so there is no newer AWS size to migrate to. 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 2 × NVIDIA RTX PRO 6000 Blackwell that is a downgrade per card, honest for inference and not for the training this instance was bought for.

Match qualityWeaker GPU

g7e.12xlarge puts 2 Blackwell, Blackwell GPUs (192 GiB VRAM) in one machine. Spot has a single GPU class and no multi-GPU node, so there is no shape here that places 2 cards behind one host. Per card the NVIDIA A30 (Ampere, 24 GB VRAM) is a step down from what g7e.12xlarge carries, and the aggregate is not reachable at all.

That makes this honest for data-parallel inference and embarrassingly parallel batch work, where each card is independent, and dishonest for anything that shards a model across cards or synchronises every step. 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 diskg7e.12xlarge comes with 1 × 3,800 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$0.191
When to use it
Good for
  • ●Model inference and embedding generation
  • ●Video transcoding and image processing
  • ●Machine learning experimentation and fine-tuning
  • ●Workloads that fit inside 512 GiB of RAM
  • ●Jobs that want 1 × 3,800 GB of local NVMe scratch space
  • ●Network-heavy services — AWS rates this size at 400 Gigabit
Avoid if
  • ●The working set needs more than 512 GiB of RAM
  • ●More than 10.67 GiB of RAM per vCPU is needed — that ratio is fixed for the whole family
  • ●The work is single-threaded. Most of these 48 vCPU would sit idle.
  • ●Anything the CPU can do fast enough. GPU hours are the most expensive on the price list.
Rackspace Spot sells spare capacity at market prices. You pay what the market decides, not a list price.
  • 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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