Instances›AWS EC2›g7›g7.24xlarge
Compute
vCPUs96
Memory384 GiB
Mem / vCPU4.00 GiB
ProcessorXeon Scalable
Clock speed3.9 GHz
Architecturex86_64
Storage
Type1 × 3,800 GB NVMe SSD
Bandwidth40,000 Mbps
Throughput5000 MB/s
IOPS160,000
Networking
Performance350 Gigabit
Max ENIs24
IPv6Yes
GPUNVIDIA RTX PRO 4500 Blackwell
Family sizes
g7.2xlarge8 vCPU · 32 GiB
g7.4xlarge16 vCPU · 64 GiB
g7.8xlarge32 vCPU · 128 GiB
g7.12xlarge48 vCPU · 192 GiB
g7.24xlarge96 vCPU · 384 GiB
g7.48xlarge192 vCPU · 768 GiB
g7.24xlarge

The g7.24xlarge pairs 96 vCPU with 384 GiB of RAM, the 4.00 GiB-per-vCPU ratio that marks a GPU instance. In the name, "g" is graphics, "7" is the generation and "24xlarge" is the size within the g7 family. It runs on Intel Xeon Scalable (Granite Rapids) at 3.9 GHz, and AWS introduced it in June 2026. g7 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 4 × NVIDIA RTX PRO 4500 Blackwell that is a downgrade per card, honest for inference and not for the training this instance was bought for.

Match qualityWeaker GPU

g7.24xlarge puts 4 GPUs (128 GiB VRAM) in one machine. Spot has a single GPU class and no multi-GPU node, so there is no shape here that places 4 cards behind one host. Per card the NVIDIA A30 (Ampere, 24 GB VRAM) is a step down from what g7.24xlarge 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 diskg7.24xlarge 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 384 GiB of RAM
  • ●Jobs that want 1 × 3,800 GB of local NVMe scratch space
  • ●Network-heavy services — AWS rates this size at 350 Gigabit
Avoid if
  • ●The working set needs more than 384 GiB of RAM
  • ●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 96 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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