Best Cloud GPU Providers with NVIDIA A100

What the NVIDIA A100 actually is

The NVIDIA A100 is a data-center accelerator built on the Ampere architecture (the GA100 GPU) and is still one of the most widely rented cards for AI and HPC workloads. It is a workhorse from the generation before Hopper (H100), and that positioning is exactly why it remains so relevant when you are renting: it is mature, broadly available across providers, and priced below the newest silicon while still being genuinely capable for serious training and inference.

The A100 ships in two memory configurations that matter a great deal when you read the comparison above:

• 40 GB variant, using HBM2 memory.
• 80 GB variant, using faster HBM2e memory, with memory bandwidth in the low 2 TB/s range versus roughly 1.5–1.6 TB/s on the 40 GB card.

Both share the same compute die, so the difference is capacity and bandwidth rather than raw arithmetic throughput. For memory-bound jobs and larger models, the 80 GB version is meaningfully better, and it is worth confirming which variant a listing offers before you commit.

Compute and precision support

The A100 introduced third-generation Tensor Cores and several features that are central to modern AI:

• TF32 for accelerated FP32-style training math without code changes.
• FP16 and BF16 mixed precision, the standard for most deep-learning training.
• INT8 (and INT4) for quantized inference throughput.
• Structural sparsity support that can roughly double effective tensor throughput on suitable models.
• FP64 on Tensor Cores, which is why the A100 is still common in scientific and HPC clusters.

One important limitation to keep in mind: the A100 predates the FP8 data type that arrived with Hopper. If your workflow specifically depends on native FP8 training or inference (common for the very newest large-model recipes), the A100 cannot do it in hardware and you should look at a newer card in the list above.

Interconnect and multi-GPU scaling

The A100 supports NVLink (third generation) and, in NVIDIA’s HGX baseboards, NVSwitch, giving high-bandwidth GPU-to-GPU communication well beyond what PCIe alone provides. This matters because large-model training and tensor/pipeline parallelism are extremely sensitive to inter-GPU bandwidth. When renting, check whether a multi-GPU node is genuinely NVLink/NVSwitch-connected or simply several PCIe cards in one box, because the difference shows up directly in scaling efficiency.

The A100 also offers Multi-Instance GPU (MIG), which partitions a single physical GPU into as many as seven isolated instances. Some providers expose MIG slices as cheaper fractional rentals, which is excellent for smaller inference jobs, notebooks, or development where a whole 40/80 GB card would be wasted.

Which workloads the A100 fits

The A100 sits in a sweet spot for a broad range of jobs:

• Training and fine-tuning mid-to-large models. The 80 GB variant comfortably handles fine-tuning of many open-weight large language models, especially with parameter-efficient methods, and multi-GPU NVLink nodes scale to full pre-training of substantial models.
• High-throughput batch inference where INT8/FP16 and large VRAM let you serve sizable models or big batches efficiently.
• Scientific computing and HPC, thanks to strong FP64 performance.

It is arguably overkill for light experimentation, small models, or intermittent real-time inference of compact networks, where a smaller or older card (or a MIG slice) is far more cost-effective. It is underpowered relative to current top-tier cards only for the largest frontier-scale training runs or workloads that hinge on FP8 and the newest interconnect generations.

Renting an A100: cost, availability, and what to check

In the rental cost spectrum, the A100 typically lands in the mid-to-upper tier: clearly above consumer cards and older data-center GPUs, but generally cheaper than the latest Hopper and Blackwell parts. Because it has been in the market for several years, supply is comparatively healthy and you will usually find it on demand across many providers, plus on spot or interruptible tiers at a discount for fault-tolerant or checkpointed jobs. Exact rates move constantly and vary by region and variant, so use the live comparison above rather than any fixed figure.

Before you rent, verify the details that actually change your results:

• Memory variant: 40 GB vs 80 GB, since this dictates the model sizes and batch sizes you can run.
• Interconnect on multi-GPU nodes: real NVLink/NVSwitch versus PCIe-only.
• Billing granularity (per-second vs per-hour) and whether spot interruptions are checkpoint-friendly for your job.
• Storage and egress: fast local NVMe for datasets and any data-transfer fees that could dwarf the GPU cost.
• Region and quota, which affect both price and how quickly you can scale to several cards.

Frequently asked questions

What is the difference between the 40 GB and 80 GB A100?

Both use the same Ampere compute die, so peak arithmetic throughput is similar. The 80 GB model uses faster HBM2e memory with higher bandwidth (around 2 TB/s) and double the capacity, which helps memory-bound jobs, larger models, and bigger batch sizes. If your model or context is large, prefer the 80 GB variant.

Is an A100 still worth renting instead of a newer GPU?

Often, yes. For most fine-tuning, mid-to-large training, and high-throughput inference, the A100 delivers strong performance at a lower rental rate and with broader availability than the newest cards. The main reasons to choose newer silicon are native FP8 support, larger per-card memory, or the absolute fastest frontier-scale training.

Can the A100 do FP8 training or inference?

No. Native FP8 hardware support arrived with the Hopper generation, after Ampere. The A100 supports TF32, FP16, BF16, INT8 and FP64, plus structural sparsity, but if your recipe requires FP8 in hardware you should select a newer GPU from the comparison above.

How many A100s do I need for multi-GPU training?

It depends on model size and parallelism strategy, but the key is choosing nodes with genuine NVLink or NVSwitch connectivity rather than PCIe-only boxes, because inter-GPU bandwidth heavily affects scaling efficiency. Confirm the interconnect and use checkpointing if you rent on interruptible/spot capacity.