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Ampere architecture

NVIDIA A100

NVIDIA A100 remains a capable, well-understood platform for AI training, inference, data analytics and HPC — with 80 GB of HBM2e memory and up to 2 TB/s of bandwidth per GPU.

Request GPU ConsultationView specifications
GPU memory
80 GB HBM2e
Memory bandwidth
2,039 GB/s
FP16 tensor
624 TFLOPS

Specifications as published by NVIDIA; Tensor Core ratings shown are sparsity-accelerated figures.

A100AMPERE ARCHITECTURE

Overview

Why A100 fits

Ampere architecture for AI and HPC

A100 won its place as the reference data center GPU of the Ampere generation. For teams running established frameworks and pipelines, it offers a strong balance of memory capacity, compute and cost profile — and it is one of the most widely supported accelerators in partner data centers.

We typically position A100 where a workload is proven, cost-efficient and does not need Hopper-era FP8 throughput or the largest memory pools: mid-size model training, batch inference, HPC simulation and analytics. It can also be a sensible entry point before moving larger workloads to Hopper-class capacity.

Specifications

A100 technical specifications

Published figures for the accelerator, followed by the variant and configuration caveats that apply.

Architecture
NVIDIA Ampere
GPU memory
80 GB HBM2e
Memory bandwidth
2,039 GB/s
FP16 / BF16 Tensor Core
624 TFLOPS (with sparsity)
TF32 Tensor Core
312 TFLOPS (with sparsity)
FP64
9.7 TFLOPS
Interconnect
NVLink 600 GB/s · PCIe Gen4 64 GB/s
Form factor
SXM
Max TDP
400 W standard
Multi-Instance GPU
Up to 7 partitions

Variant & configuration note

Rows describe the A100 80GB SXM as published by NVIDIA. The 80GB PCIe variant runs at 1,935 GB/s and 300 W, and 40 GB variants exist with different memory and bandwidth. HGX boards carry 4, 8 or 16 GPUs per system. Host configuration 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 A100 fits

Workload shapes this platform suits — the scoping conversation maps them to a concrete configuration.

  • Training mid-size models

    Supervised training, vision and language models where 80 GB per GPU provides comfortable headroom without Hopper pricing.

  • Production inference

    Always-on model serving, batch scoring and recommendation workloads with mature MIG-based partitioning options.

  • HPC and data science

    Double-precision simulation, CUDA-accelerated analytics and RAPIDS pipelines on a widely certified platform.

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

H100HOPPER ARCHITECTUREHopper

NVIDIA H100

The proven Hopper accelerator for LLM training and inference, with FP8 Transformer Engine support and 900 GB/s NVLink scale-up.

GPU memory
80 GB HBM3
Memory bandwidth
3.35 TB/s
FP8 tensor
3,958 TFLOPS
View specifications
A30A30AMPERE ARCHITECTUREAmpere

NVIDIA A30

A right-sized Ampere accelerator for teams stepping up from CPU inference or low-end GPUs into real AI capacity.

GPU memory
24 GB HBM2
Memory bandwidth
933 GB/s
Max power
165 W
View specifications
H200HOPPER ARCHITECTUREHopper

NVIDIA H200

Hopper-class acceleration with the largest HBM3e buffer in the catalogue, for AI workloads that are constrained by memory.

GPU memory
141 GB HBM3e
Memory bandwidth
4.8 TB/s
FP8 tensor
3,958 TFLOPS
View specifications
Browse all GPU instances

FAQ

A100 — common questions

Is A100 still a sensible choice in the current generation?
For proven workloads, often yes — especially where a model has been tuned around Ampere and the cost profile matters more than peak throughput. Where FP8 training or larger memory pools would change outcomes, we will recommend Hopper-class capacity instead and explain why.
Should I take a 40 GB or 80 GB variant?
The larger 80 GB variant gives more room per GPU for models and larger batches, and doubles the size of each MIG slice. If your workload fits comfortably in 40 GB, the smaller variant can be more economical. We size this against your actual models rather than generically.
Can existing A100 workloads be migrated to Scalenta-managed capacity?
That is a common starting point. We review the current environment, performance profile and dependencies, then propose a migration path with a rollback position — the same discipline we apply to all infrastructure migrations.

Request a GPU consultation

Talk to us about NVIDIA A100 — workload-first scoping, written findings and a proposal with clear scope. No obligation.

Request Infrastructure AssessmentTalk to our team