g5g.4xlarge is AWS's GPU EC2 size at 16 vCPU and 32 GiB. In the name, "g" is graphics, "5" is the generation, "g" marks AWS Graviton (ARM) processors and "4xlarge" 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; g6.4xlarge is the direct replacement at this size, scoring 332,519 CoreMark/s against this instance's 314,878 — 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.4xlarge pairs 1 Turing GPU (16 GiB VRAM) with 16 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.4xlarge. 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.4xlarge 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
1 hr
Region
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AWS Spot
1-yr Reserved
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 32 GiB of RAM
●Parallel work that can occupy 16 vCPU
●Network-heavy services — AWS rates this size at Up to 10 Gigabit
Avoid if
●Starting a new deployment. g6.4xlarge is the newer generation at this size.
●The working set needs more than 32 GiB of RAM
●You cannot rebuild x86-only binaries or vendor images for aarch64
●The work is single-threaded. Most of these 16 vCPU would sit idle.
●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.
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.
