Instances›AWS EC2›g4ad›g4ad.8xlarge
Compute
vCPUs32
Memory128 GiB
Mem / vCPU4.00 GiB
ProcessorEPYC 7R32
Clock speed2.8 GHz
Architecturex86_64
Storage
TypeEBS only
BandwidthN/A
ThroughputN/A
IOPS0
Networking
Performance15 Gigabit
Max ENIsN/A
IPv6Yes
GPUAMD Radeon Pro V520
Family sizes
g4ad.xlarge4 vCPU · 16 GiB
g4ad.2xlarge8 vCPU · 32 GiB
g4ad.4xlarge16 vCPU · 64 GiB
g4ad.8xlarge32 vCPU · 128 GiB
g4ad.16xlarge64 vCPU · 256 GiB
g4ad.8xlarge

g4ad.8xlarge is AWS's GPU EC2 size at 32 vCPU and 128 GiB. In the name, "g" is graphics, "4" is the generation, "ad" marks AMD processors and local NVMe storage and "8xlarge" is the size within the g4ad family. It runs on AMD EPYC 7R32 at 2.8 GHz, and AWS introduced it in December 2020. AWS has since shipped g5, g6 and g7; g5.8xlarge is the direct replacement at this size. 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 × AMD Radeon Pro V520 that is a downgrade per card, honest for inference and not for the training this instance was bought for.

Match qualityWeaker GPU

g4ad.8xlarge puts 2 GPUs (16 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 g4ad.8xlarge 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.

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
  • ●Remote graphics workstations and rendering
  • ●CUDA-accelerated batch jobs
  • ●Model inference and embedding generation
  • ●Workloads that fit inside 128 GiB of RAM
  • ●Parallel work that can occupy 32 vCPU
  • ●Network-heavy services — AWS rates this size at 15 Gigabit
Avoid if
  • ●Starting a new deployment. g5.8xlarge is the newer generation at this size.
  • ●The working set needs more than 128 GiB of RAM
  • ●Local NVMe or instance-store disk is a hard requirement
  • ●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 32 vCPU would sit idle.
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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