The g5g.8xlarge is a GPU AWS EC2 instance: 32 vCPU and 64 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 "8xlarge" 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; g6.8xlarge is the direct replacement at this size, scoring 689,915 CoreMark/s against this instance's 622,245 — roughly 11% 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.8xlarge pairs 1 Turing GPU (16 GiB VRAM) with 32 vCPU. Spot's GPU node uses the NVIDIA A30 (Ampere, 24 GB VRAM) with 24 vCPU and 64 GiB of memory — newer hardware with more GPU memory, but 24 vCPU against 32. For inference, batch transcoding and single-card fine-tuning the card is the upgrade; anything CPU-bound running alongside it gets less to work with. 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.8xlarge 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
AWS on-demand
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
●Model inference and embedding generation
●Video transcoding and image processing
●Machine learning experimentation and fine-tuning
●Workloads that fit inside 64 GiB of RAM
●Parallel work that can occupy 32 vCPU
●Network-heavy services — AWS rates this size at 12 Gigabit
Avoid if
●Starting a new deployment. g6.8xlarge is the newer generation at this size.
●The working set needs more than 64 GiB of RAM
●You cannot rebuild x86-only binaries or vendor images for aarch64
●The work is single-threaded. Most of these 32 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.
