The g5g.xlarge pairs 4 vCPU with 8 GiB of RAM, the 2.00 GiB-per-vCPU ratio that marks a GPU instance. In the name, "g" is graphics, "5" is the generation, "g" marks AWS Graviton (ARM) processors and "xlarge" 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.xlarge is the direct replacement at this size, scoring 83,814 CoreMark/s against this instance's 77,504 — roughly 8% 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.xlarge pairs 1 Turing GPU (16 GiB VRAM) with 4 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.xlarge. 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.xlarge 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
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US West, San Jose
$0.191
When to use it
Good for
●CUDA-accelerated batch jobs
●Model inference and embedding generation
●Video transcoding and image processing
●Workloads that fit inside 8 GiB of RAM
●Small, steady services that do not need more than 4 vCPU
●Network-heavy services — AWS rates this size at Up to 10 Gigabit
Avoid if
●Starting a new deployment. g6.xlarge is the newer generation at this size.
●The working set needs more than 8 GiB of RAM
●You cannot rebuild x86-only binaries or vendor images for aarch64
●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
●Local NVMe or instance-store disk is a hard requirement
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.
