Instances›AWS EC2›g2›g2.8xlarge
Compute
vCPUs32
Memory60 GiB
Mem / vCPU1.88 GiB
ProcessorXeon E5-2670
Clock speed2.6 GHz
Architecturex86_64
Storage
TypeEBS only
BandwidthN/A
ThroughputN/A
IOPS0
Networking
PerformanceHigh
Max ENIsN/A
IPv6No
GPUNVIDIA GRID K520
Family sizes
g2.2xlarge8 vCPU · 15 GiB
g2.8xlarge32 vCPU · 60 GiB
g2.8xlarge

The g2.8xlarge pairs 32 vCPU with 60 GiB of RAM, the 1.88 GiB-per-vCPU ratio that marks a GPU instance. In the name, "g" is graphics, "2" is the generation and "8xlarge" is the size within the g2 family. It runs on Intel Xeon E5-2670 (Sandy Bridge) at 2.6 GHz, and AWS introduced it in April 2015. AWS has since shipped g3, g4ad, g5 and g6; g3.8xlarge 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 4 × NVIDIA GRID K520 that is a downgrade per card, honest for inference and not for the training this instance was bought for — it prices about 93% below the US West (N. California) on-demand rate.

Match qualityWeaker GPU

g2.8xlarge puts 4 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 4 cards behind one host. Per card the NVIDIA A30 (Ampere, 24 GB VRAM) is a step down from what g2.8xlarge 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$2.808$0.191
$2.617 savings
When to use it
Good for
  • ●Remote graphics workstations and rendering
  • ●CUDA-accelerated batch jobs
  • ●Model inference and embedding generation
  • ●Workloads that fit inside 60 GiB of RAM
  • ●Parallel work that can occupy 32 vCPU
Avoid if
  • ●Starting a new deployment. g3.8xlarge is the newer generation at this size.
  • ●The working set needs more than 60 GiB of RAM
  • ●Anything the CPU can do fast enough. GPU hours are the most expensive on the price list.
  • ●More than 1.88 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.
  • ●Local NVMe or instance-store disk is a hard requirement
Stop overpaying AWS. This compute costs $2,050/mo on AWS vs $139/mo on Rackspace Spot.
  • 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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