The g5g.2xlarge is a GPU AWS EC2 instance: 8 vCPU and 16 GiB of memory, 2.00 GiB per vCPU. In the name, "g" is graphics, "5" is the generation, "g" marks AWS Graviton (ARM) processors and "2xlarge" is the size within the g5g family. It runs on AWS Graviton2 Processor at 2.5 GHz, and AWS introduced it in November 2021. AWS has since shipped g6 and g7; g6.2xlarge is the direct replacement at this size, scoring 167,347 CoreMark/s against this instance's 157,801 — roughly 6% 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 T4G Tensor Core that is a fair swap for inference and media work — the A30 carries more memory and more throughput per card.
Match qualityComparable GPU
g5g.2xlarge pairs 1 Turing GPU (16 GiB VRAM) with 8 vCPU. Spot's GPU node uses the NVIDIA A30 (Ampere, 24 GB VRAM) with 24 vCPU and 64 GiB of memory — more GPU memory, more CPU and newer hardware than g5g.2xlarge. For inference, batch transcoding and single-card fine-tuning that is an upgrade rather than a compromise. 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.
x86_64 onlyg5g.2xlarge is Graviton (arm64) and Rackspace Spot has no arm64 nodes, so you'd be moving to x86. You'll need x86 builds of your images, and per-core speed differs between the chips — worth testing before you decide how many nodes to run.
Pricing by region
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Region
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3-yr Reserved
Rackspace Spot
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US West, San Jose
$0.191
When to use it
Good for
●Machine learning experimentation and fine-tuning
●Remote graphics workstations and rendering
●CUDA-accelerated batch jobs
●Workloads that fit inside 16 GiB of RAM
●Parallel work that can occupy 8 vCPU
●Network-heavy services — AWS rates this size at Up to 10 Gigabit
Avoid if
●Starting a new deployment. g6.2xlarge is the newer generation at this size.
●The working set needs more than 16 GiB of RAM
●You cannot rebuild x86-only binaries or vendor images for aarch64
●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 2.00 GiB of RAM per vCPU is needed — that ratio is fixed for the whole family
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.
