Instances›AWS EC2›g6e›g6e.24xlarge
Compute
vCPUs96
Memory768 GiB
Mem / vCPU8.00 GiB
ProcessorEPYC 7R13 Processor
Clock speed2.6 GHz
Architecturex86_64
Storage
Type2 × 1,900 GB NVMe SSD
Bandwidth30,000 Mbps
Throughput3750 MB/s
IOPS120,000
Networking
Performance200 Gigabit
Max ENIs20
IPv6Yes
GPUNVIDIA L40S
Family sizes
g6e.xlarge4 vCPU · 32 GiB
g6e.2xlarge8 vCPU · 64 GiB
g6e.4xlarge16 vCPU · 128 GiB
g6e.8xlarge32 vCPU · 256 GiB
g6e.12xlarge48 vCPU · 384 GiB
g6e.16xlarge64 vCPU · 512 GiB
g6e.24xlarge96 vCPU · 768 GiB
g6e.48xlarge192 vCPU · 1536 GiB
g6e.24xlarge

The g6e.24xlarge pairs 96 vCPU with 768 GiB of RAM, the 8.00 GiB-per-vCPU ratio that marks a GPU instance. In the name, "g" is graphics, "6" is the generation, "e" marks extended capacity and "24xlarge" is the size within the g6e family. It runs on AMD EPYC 7R13 Processor at 2.6 GHz, and AWS introduced it in August 2024. AWS has since shipped g7e; g7e.24xlarge is the direct replacement at this size, scoring 2,040,961 CoreMark/s against this instance's 1,793,471 — roughly 14% 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 4 × NVIDIA L40S that is a downgrade per card, honest for inference and not for the training this instance was bought for.

Match qualityWeaker GPU

g6e.24xlarge puts 4 Ada Lovelace, Ada Lovelace, Ada Lovelace, Ada Lovelace GPUs (192 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 g6e.24xlarge 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.

No local diskg6e.24xlarge comes with 2 × 1,900 GB NVMe SSD attached to the machine. Spot nodes use network storage only, so that disk doesn't come with it. If you were using it as fast scratch space, you'd move to a persistent volume or object storage, which are slower to read.
Pricing by region
1 hr
RegionAWS on-demandAWS Spot1-yr Reserved3-yr ReservedRackspace SpotYou save
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 768 GiB of RAM
  • ●Jobs that want 2 × 1,900 GB of local NVMe scratch space
  • ●Network-heavy services — AWS rates this size at 200 Gigabit
Avoid if
  • ●The working set needs more than 768 GiB of RAM
  • ●The work is single-threaded. Most of these 96 vCPU would sit idle.
  • ●Anything the CPU can do fast enough. GPU hours are the most expensive on the price list.
  • ●More than 8.00 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.
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