An A100 cloud Europe search makes sense when you want a proven datacenter GPU for fine-tuning or inference, inside the EU, without paying for the newest generation. On Cloudj, A100s come from the same live inventory of independent host machines as every other GPU, and you find European ones by filtering by country code. This guide covers the two A100 variants on the list, what their 40 GB of memory realistically handles, how to search EU countries and how to decide between an A100 and an H100.
The GPU cloud Europe guide covers the region as a whole.
The two A100s on the list
| A100 PCIe | A100 SXM4 | |
|---|---|---|
| Version listed | 40 GB | 40 GB |
| Architecture | Ampere | Ampere |
| Form factor | PCIe card | SXM4 module on a multi-GPU board |
| GPU-to-GPU link (general background) | Over PCIe, sometimes with NVLink bridges | Typically NVLink on the board |
| Best suited to | Single-GPU fine-tuning, inference, evaluation | Multi-GPU training that syncs gradients often |
The A100 was also sold in an 80 GB version, but the A100s on Cloudj's GPU list are listed in the 40 GB version. Plan with 40 GB per GPU. Each model page shows the live "from" price and whether it is in stock right now; the two variants are stocked separately, so one can be listed in a country while the other is not.
What 40 GB handles
These are rough guides for planning, not benchmarks:
- LoRA fine-tuning of 7B-13B models in bf16 usually fits on one 40 GB card with sensible batch sizes and gradient checkpointing.
- QLoRA (4-bit base weights) extends single-card fine-tuning to larger models, roughly up to the 30B range, depending on sequence length.
- Inference of a 4-bit 70B model needs roughly 40 GB for weights alone, plus cache, so plan on two A100s rather than one.
- Full fine-tuning of anything beyond small models needs several GPUs, which is where SXM4 machines with 4x or 8x GPUs come in.
Ampere supports bf16, which most modern training recipes use. It does not have the FP8 support of Hopper cards, which matters only if your framework actually uses FP8.
Searching A100 cloud Europe listings by country
Open the deploy page with the model preselected, deploy an A100 PCIe, and enter the EU countries you accept in the Country field:
DE, NL, FR, SEResults are sorted by total price and show each machine's country (never the city), reliability, max duration, Datacenter badge, storage and bandwidth prices. If a machine is listed, you can deploy it. If not, switch to the SXM4 variant, change the GPU count, add EU codes such as FI, BE or PL, loosen filters, or check back later; stock changes during the day. The country filter walkthrough explains every field.
Before any of this, check your account level. A100s are datacenter GPUs and need a higher account level than a new account has. If the console shows "This GPU class is not available at your account level", contact Cloudj support from the console.
A100 or H100: a value decision
The H100 is the newer, more capable card, and the H100 cloud Europe guide covers how to rent one. But "more capable" does not mean "better value" for every job. Think in cost per finished job: hourly price multiplied by hours to finish.
| Your job | Lean towards | Why |
|---|---|---|
| LoRA or QLoRA on a model that fits in 40 GB | A100 | The job fits; a faster card may not save enough hours to offset its price |
| Many small fine-tunes or evaluation runs | A100 | Throughput per run matters less than running several in parallel |
| Training that needs more than 40 GB per GPU | H100 (80 GB) | Avoids splitting the model across more cards |
| Workloads that use FP8 or Hopper-specific features | H100 | Ampere does not have them |
| Deadline-bound training where hours matter more than cost | H100 | Finishing sooner is the point |
Compare live prices on the two model pages and estimate hours from a short test run on each. Per-minute billing makes a one-hour comparison cheap. The A100 vs H100 comparison goes into more detail.
Running a fine-tune safely on an A100
Every listing is an independent host with no hardware SLA, and each machine has an "available until" date after which the instance and its disk are removed. For a fine-tune that runs for hours or days:
- Check the max duration covers your run plus time to copy results off.
- Size the disk at deploy for base model, dataset, checkpoints and headroom; it cannot be resized later.
- Save checkpoints on a schedule and copy them to storage you control (command below).
- Destroy the instance when you are done; stopping still charges storage and releases the GPU.
rsync -avP -e "ssh -p <port>" root@<host>:/workspace/checkpoints/ ./checkpoints/If the host goes offline, billing pauses but the instance does not move, so your last checkpoint is your restart point. A complete workflow is in LLM fine-tuning on GPUs in Europe.
Data location
EU codes keep the host machine in the countries you pick. Cloudj makes no compliance claim for itself or for hosts, so check your own legal obligations before processing personal data.
FAQ
Are the A100s 40 GB or 80 GB?
The A100s on the GPU list are listed in the 40 GB version.
Should I pick PCIe or SXM4?
PCIe for single-GPU work; SXM4 for multi-GPU training where GPUs communicate heavily. If your job is single-GPU, search both.
Why can't I deploy an A100?
Datacenter GPUs need a higher account level. Contact Cloudj support from the console.
Is there always an A100 in Germany?
No. Search with DE; if nothing is listed, add other EU codes or try again later.
Search for an A100 in the EU
Open the A100 PCIe deploy page, enter DE, NL, FR, SE, and try SXM4 if PCIe returns nothing. Live "from" prices for every model are on GPU cloud.