Ampere architecture
NVIDIA A30
NVIDIA A30 brings 24 GB of HBM2 memory and third-generation Tensor Cores into a mainstream 165 W PCIe card — a practical middle ground for training, inference and HPC in standard servers.
- GPU memory
- 24 GB HBM2
- Memory bandwidth
- 933 GB/s
- Max power
- 165 W
Specifications as published by NVIDIA; Tensor Core ratings shown are sparsity-accelerated figures.
Overview
Why A30 fits
Ampere architecture for mainstream AI
A30 exists for the workloads between entry-level and flagship: medium models, production inference that has outgrown smaller cards, and analytics pipelines that benefit from real GPU memory bandwidth at a manageable power draw. Its 24 GB HBM2 buffer and 933 GB/s of bandwidth punch well above its power class.
It suits organisations standardising AI capacity across a number of standard servers rather than committing to a large dedicated AI cluster. Capacity is sourced through verified partners, with configurations confirmed per engagement.
Specifications
A30 technical specifications
Published figures for the accelerator, followed by the variant and configuration caveats that apply.
- Architecture
- NVIDIA Ampere
- GPU memory
- 24 GB HBM2
- Memory bandwidth
- 933 GB/s
- Tensor cores
- Third-generation
- Interconnect
- PCIe Gen4 x16
- Form factor
- PCIe, dual-slot
- Max TDP
- 165 W
Variant & configuration note
Single PCIe accelerator profile as published by NVIDIA. Host server configuration — CPU, memory, storage and card count — is scoped per engagement.
Specifications describe the GPU itself as published by NVIDIA — not a fixed server configuration. Scalenta scopes complete instances (GPU count, interconnect, CPU, memory, storage) per engagement and states them in writing.
Use cases
Where A30 fits
Workload shapes this platform suits — the scoping conversation maps them to a concrete configuration.
Mid-range training and fine-tuning
Vision, language and tabular models where 24 GB of HBM2 gives real headroom without flagship-class power or cooling.
Production inference
Steady-state model serving in standard servers, with multiple cards per host for density.
Analytics acceleration
GPU-accelerated data processing and classic machine learning on structured data, alongside CUDA and RAPIDS libraries.
Delivery model
Partner-sourced, scoped in writing
How GPU capacity is delivered at Scalenta — the same three commitments on every model page.
Partner-sourced capacity
Accelerator capacity is sourced through verified data center and hardware partners — no facility-ownership claims, no stock promises.
Scoped per engagement
Model, variant, GPU count, interconnect and host configuration are confirmed in writing before anything is committed.
Operated with 24/7 support
Deployments are covered by 24/7 support and SLA-backed managed services scoped to your environment.
Related GPU models
Nearby options in the catalogue
NVIDIA A100
A proven training-and-HPC accelerator with an 80 GB memory pool per GPU and mature tooling across the AI ecosystem.
- GPU memory
- 80 GB HBM2e
- Memory bandwidth
- 2,039 GB/s
- FP16 tensor
- 624 TFLOPS
NVIDIA A2
Entry-level, single-slot Ampere acceleration for inference and edge workloads that do not justify flagship hardware.
- GPU memory
- 16 GB GDDR6
- Memory bandwidth
- 200 GB/s
- Max TDP
- 40–60 W
NVIDIA L4
The efficiency play: dense video processing and AI inference in a low-profile card built for many-servers, many-streams deployments.
- GPU memory
- 24 GB GDDR6
- Memory bandwidth
- 300 GB/s
- Max power
- 72 W
FAQ
A30 — common questions
Is A30 a training card or an inference card?
How does A30 compare with the A2 for entry projects?
Can A30s be pooled across a team?
Request a GPU consultation
Talk to us about NVIDIA A30 — workload-first scoping, written findings and a proposal with clear scope. No obligation.