Instances›AWS EC2›dl2q›dl2q.24xlarge
Compute
vCPUs96
Memory768 GiB
Mem / vCPU8.00 GiB
ProcessorXeon Platinum 8259
Clock speed2.5 GHz
Architecturex86_64
Storage
TypeEBS only
Bandwidth19,000 Mbps
Throughput2375 MB/s
IOPS80,000
Networking
Performance100 Gigabit
Max ENIs15
IPv6Yes
GPUQualcomm AI 100 Accelerators
Family sizes
dl2q.24xlarge96 vCPU · 768 GiB
dl2q.24xlarge

The dl2q.24xlarge pairs 96 vCPU with 768 GiB of RAM, the 8.00 GiB-per-vCPU ratio that marks a machine-learning accelerator instance. In the name, "dl" is deep learning, "2" is the generation, "q" marks Qualcomm accelerators and "24xlarge" is the size within the dl2q family. It runs on Intel Xeon Platinum 8259 (Cascade Lake) at 2.5 GHz, and AWS introduced it in November 2023. dl2q is the current generation of the dl family, so there is no newer AWS size to migrate to. Rackspace Spot has no equivalent to offer: the Inferentia and Trainium accelerators are AWS-exclusive silicon with no third-party equivalent. Spot's largest VM tops out at 16 vCPU and 128 GiB, and even that is not the missing piece here. The table below prices what AWS charges; the honest answer on the Spot side is that this workload stays where it is.

Match qualityNo equivalent

dl2q.24xlarge is 8 GPUs (128 GiB VRAM) wired together inside one chassis. The interconnect is what you bought — that is what makes it one accelerator with pooled memory rather than several machines — and Rackspace Spot has nothing that reproduces it. The Spot GPU class is built on the NVIDIA A30 (Ampere, 24 GB VRAM), and separate nodes of it on ordinary datacentre networking are not a substitute for a single interconnect-joined node.

Split the job across independent single-GPU nodes only if it is data-parallel with no per-step synchronisation and the model fits one card. Model-parallel work, or anything synchronising gradients every step, does not survive the move. Rackspace Spot sells x86_64 Kubernetes nodes up to 16 vCPU, plus one NVIDIA A30 GPU class.

Pricing by region
1 hr
RegionAWS on-demandAWS Spot1-yr Reserved3-yr ReservedRackspace SpotYou save
US East, N. Virginia
US Central, Chicagono AWS region
US Central, Dallasno AWS region
Hong Kong
Sydney, Australia
London, UK
US West, San Jose
US Central, Dallas 2no AWS region
US East, N. Virginia 2
When to use it
Good for
  • ●Workloads already built against the Neuron SDK
  • ●Cost-per-inference optimisation inside AWS
  • ●Inference or training on AWS's own accelerator silicon
  • ●Workloads that fit inside 768 GiB of RAM
  • ●Parallel work that can occupy 96 vCPU
  • ●Network-heavy services — AWS rates this size at 100 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.
  • ●Local NVMe or instance-store disk is a hard requirement
  • ●Frameworks that have not been compiled for the Neuron SDK.
  • ●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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