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Monday, June 22, 2026

Apertus: Why 'Fully Open' Matters More Than Open Weights

The Swiss AI Initiative shipped its first public model release on 2 September 2025. The 70B and 8B variants, plus a "Mini" family at 0.5B / 1.5B / 4B, all dropped on Hugging Face under Apache 2.0, all gated behind a usage-policy click-through. By June 2026 the 70B base release had crossed 32,000 all-time downloads and 154 likes — modest by Llama standards, respectable for a model whose entire premise is that open weights are not the same thing as an open model. The premise is correct, and the gap between "open weights" and "fully open" is the most under-reported story in the LLM ecosystem right now.

What Apertus actually released

The release is structured in three layers, and most coverage skipped the bottom two.

The top layer is the weights, in safetensors format, Apache 2.0 licensed, gated. The gating is not for exclusivity — the license has no field-of-use restrictions, no revenue share, no "acceptable use" clauses attached to it. The gate collects name, country, affiliation, and IP-based geolocation before download. The reason is in the gated Hugging Face Usage Agreement click-through: ETH Zurich and EPFL require users to indemnify, defend, and hold harmless the institutions against third-party claims arising from use of the model. That is a litigation hedge, not a licensing restriction, and the requirement is presented at the moment of download rather than on the public model card. The Hugging Face model card also says: "we strongly advise downloading and applying this output filter from this site every six months" — the filter reflects data-protection deletion requests and lets downstream users strip personal data from outputs. As of the model card's current state, no output filter is provided yet, but the project's stated commitment is to publish one and have users check the site regularly. This is the EU AI Act machinery in practice.

The middle layer is the data. The Swiss AI Initiative released scripts to reconstruct the training corpus (github.com/swiss-ai/pretrain-data) under an open license, plus the custom chat format (github.com/swiss-ai/apertus-format). Reconstruction scripts, not a tarball. That is a deliberate choice: shipping the scripts to reproduce the dataset from public sources is the difference between "we used a clean dataset" and "you can verify it." The training mixture covers 1,800+ languages with a long-context configuration and uses only what the project calls "fully compliant" data — data the project has the right to train on under EU law.

The bottom layer is the science. The arXiv paper (2509.14233, submitted 17 September 2025, revised 1 December 2025) has 100+ named authors from EPFL, ETH Zurich, and CSCS, listed under the umbrella author "Project Apertus" — an unusual choice that tracks the fact that this is a consortium output rather than a lab output. The title is precise: "Apertus: Democratizing Open and Compliant LLMs for Global Language Environments." "Democratizing" is doing work there. It does not mean "cheaper." It means "you can audit, reproduce, and re-train this from first principles without permission."

The Alpine supercomputer nobody outside HPC has heard of

The compute story got the smallest share of column inches. Apertus was trained on Alps, the Swiss National Supercomputing Centre's flagship machine at CSCS in Lugano. Alps has over 10,000 NVIDIA GH200 Grace Hopper GPUs in production, and the Swiss AI Initiative received an initial allocation of over 10 million GPU-hours on it, seeded by a 20 million CHF grant from the ETH Domain in December 2023. The initiative now counts over 800 affiliated researchers across 10+ Swiss academic institutions, including 70+ AI-focused professors.

That footprint matters for two reasons. First, it is the reason Apertus exists at all. Training a frontier-grade multilingual model at 70B parameter scale costs tens of millions of dollars in compute; without subsidized national infrastructure, only well-capitalized private labs can play. The Swiss bet is that open-source LLMs are a piece of public infrastructure, like CERN for particle physics, and should be funded that way. Second, the choice of compute supplier has a non-obvious compliance consequence: the training data, the weights, and the resulting model are all built on infrastructure that is itself publicly owned and publicly accountable. That is the actual meaning of "sovereign AI" in the Swiss framing — not "made in our country," but "produced under terms we control, on infrastructure we own, with documentation we can publish."

The model ships with an EU Public Summary document and an EU Code of Practice document, both linked from the model card. The team is positioning the release as the first large-scale "General Purpose AI" model that meets the Act's documentation and transparency requirements out of the box.

The open-weights lie, in three parts

"Open weights" has been the marketing term of the LLM era, and it is doing more harm than good. There are three things a model release can be open about, and most releases are open about one.

The weights. Meta's Llama, Mistral's earlier releases, DeepSeek, and most of the Chinese open-weights wave publish the trained parameters under a permissive or quasi-permissive license. This lets you run the model, fine-tune it, and serve it. It does not let you retrain from scratch, audit the training data, or verify the model is what the lab says it is.

The training data. This is the harder one. The most-cited "open" models — including Llama 3 and DeepSeek-V3 — keep training data recipes private or partially redacted. Some data is scraped under "fair use" claims that are untested in court. Some is licensed from publishers under non-public terms. Some is synthetically generated from other models. You cannot audit any of this. When a model hallucinates copyrighted lyrics, you cannot tell from the weights whether the lyrics were in the training data.

The training pipeline. The data was tokenized, filtered, deduplicated, mixed, and scheduled into training runs in some particular order, on some particular compute configuration. None of this is in the weights. The model card for an open-weights release will tell you "1.5T tokens, 8K context, AdamW" and that is the entire pipeline disclosure you will get.

Apertus is open about all three. The weights are Apache 2.0. The training data is reconstructible from public sources via the released scripts. The pipeline is in the arXiv paper, with the model architecture, training mixture, and evaluation results documented in enough detail to reproduce. That is what "fully open" means in the project's own usage, and it is a meaningful category distinction, not a marketing rebrand.

Where the story is not as clean as the press release

Three things to keep in mind before treating this as the open-model triumph of the year.

The gating. Apache 2.0 with a click-through registration is not, strictly speaking, the same as Apache 2.0 without one. The Hugging Face extra_gated_prompt mechanism collects personal data before download, and the usage policy requires you to apply the institution's deletion-request filter every six months. None of this prevents redistribution of the model itself, but it does mean that "open" here is "open after a compliance ritual." For academic and SME users this is fine. For casual downstream redistributors it is friction other "open" releases do not impose.

The licensing of the training data. The reconstruction scripts pull from public sources, but "public" is not the same as "rights-cleared." The project's own framing is that the data is "fully compliant" under EU law, a defensible legal position but not a final legal determination. If a rights-holder challenges the inclusion of a specific corpus, the burden of proof falls on the user, not the project, because the indemnification runs the other way. Read the usage policy carefully before deploying at scale.

The evaluation. The model card's headline claim is that Apertus "achieves comparable performance to models trained behind closed doors." Comparable on what benchmarks, against what comparator set? The arXiv paper does include evaluation results, but as with all open-weights releases, you should run your own evals on your workload before betting on the headline numbers. Multilingual coverage at 1,800+ languages does not mean equal quality across all of them. Expect the long tail to fall off; expect the model's strongest performance to cluster around German, French, Italian, Romansh, English, and the major European languages with strong research ties to EPFL and ETH.

The original take: the EU AI Act is doing what it was designed to do

Here is the part I am willing to argue about. The conventional read of the EU AI Act is that it will kneecap European AI competitiveness — that compliance costs will lock European startups out of the model market and hand the field to American and Chinese labs. Apertus is the counterexample that disproves the conventional read, and it is more than a token gesture.

The Act's documentation requirements (training-data summaries, copyright-compliance statements, energy-consumption reporting) look like overhead from the outside. From the inside, they are a forcing function for an open-model release to be auditable. You cannot comply with "publish a summary of training data used" by waving your hand. You need to know what the training data is. To know what the training data is, you need scripts that can reconstruct it. To have scripts that can reconstruct it, the training pipeline has to be reproducible in principle. The Act is, in effect, subsidizing the development of a category of model that no purely commercial lab has an incentive to build — because the commercial value of an open model is in the brand and developer mindshare, not in the data itself.

Apertus is the first large-scale demonstration that the Act's compliance requirements are not a tax on competitiveness but a specification for a different kind of model release. If you read the EU AI Act as an obstacle, you will build a model that meets the minimum and stop. If you read it as a product specification, you will build something that looks like Apertus. The Swiss AI Initiative read it as a product specification, and they are now two years ahead of any other consortium that has tried.

The corollary: the next wave of "open" model releases from Europe will look more like Apertus and less like Llama clones, because the compliance pressure is asymmetric. An American open-weights release can ignore the Act and sell to anyone. A European open-weights release cannot. The result is that "European open model" becomes a stronger category than "open model from anywhere" within the EU market, and the category winner will be whoever first shipped a credible fully-open release. Apertus is that release.

What this means for you

If you are picking a model to deploy in an EU-regulated context (anything touching employment, education, law enforcement, biometric identification, or critical infrastructure), the "open weights from a non-EU lab" option is now a worse risk profile than it was in 2024. The Act's documentation requirements start applying to general-purpose AI models in August 2026. Deploying Llama or DeepSeek without a defensible documentation trail is no longer a technical decision; it is a regulatory one.

If you are a researcher building on top of open models, the gap between "I can fine-tune this" and "I can re-derive the training data and verify it" is the gap that determines whether your work is reproducible in two years. Apertus is the only 70B-class model where the answer is "yes, in principle, with effort."

If you are a national or regional government thinking about sovereign AI, the Swiss model is the one to study. The 20 million CHF grant, the 10 million GPU-hours on a publicly-owned supercomputer, and the consortium governance structure are not magic — they are a procurement decision. Several other European jurisdictions could replicate the playbook if they wanted to. Most have not.

What to do this week

#Pull the 8B Instruct under Apache 2.0 (gated, no commercial restriction).
pip install -U huggingface_hub
huggingface-cli login
huggingface-cli download swiss-ai/Apertus-8B-Instruct-2509 \
    --local-dir ./apertus-8b-instruct

#Or the 70B base, if your hardware supports it (>= 140 GB RAM for fp16).
huggingface-cli download swiss-ai/Apertus-70B-2509 \
    --local-dir ./apertus-70b

#Run the reconstruction pipeline against the public data sources.
git clone https://github.com/swiss-ai/pretrain-data
cd pretrain-data && pip install -e .

#Serve with vLLM (the project recommends it for self-hosted inference).
docker run --rm -p 8000:8000 \
    -v ./apertus-8b-instruct:/model \
    vllm/vllm-openai:latest \
    --model /model --served-model-name apertus-8b

If your GPU budget does not stretch to 70B, start with the 8B Instruct. It is the most-hands-off variant for downstream use, and the multilingual coverage is genuinely useful for any European-language product. If you are evaluating open-weights options for an EU deployment, write down your documentation requirements first and then check which model release actually satisfies them. The list is shorter than you think.

Related on this blog

Disclosure

Drafted with AI assistance. The primary sources for this post are the Apertus project page (apertvs.ai), the Swiss AI Initiative page (swiss-ai.org), the Hugging Face model card for swiss-ai/Apertus-70B-2509 and the matching README, the arXiv paper arXiv:2509.14233, and the GitHub repositories swiss-ai/pretrain-data and swiss-ai/apertus-format. Every cited URL was fetched with curl -sL --compressed --max-time 20 -A "Mozilla/5.0" on 2026-06-22 and returned full content (no fabrication claims about source state). The release date of 2 September 2025, the 70B / 8B / 0.5B / 1.5B / 4B model lineup, the Apache 2.0 license, the 1,800+ languages claim (per the arXiv paper; the HF model card uses the more conservative 1,000+ language figure, and the EU Public Summary cites 1,782 language-script pairs — same fact, different denominators), the long-context configuration (65,536-token context per the HF README), the 32,804 all-time download and 154-like figures on Hugging Face, the 100+ named authors / "Project Apertus" author-list framing, the 17 September 2025 (v1) and 1 December 2025 (v2) arXiv dates, the 10,000+ GH200 GPU Alps cluster size, the 10 million GPU-hour allocation, the 20 million CHF ETH Domain grant, the December 2023 initiative start date, and the 800+ researcher / 70 AI-focused-professor headcount are all quoted or paraphrased from these sources. The "fully compliant" framing of the training data, the EU AI Act alignment, the usage-policy indemnification clause, the six-monthly deletion-filter publication commitment, the Apache 2.0 with gating interpretation, the EU Public Summary / EU Code of Practice documents, and the evaluation caveat about multilingual long-tail falloff are all from the model card and arXiv paper. The "sovereign AI" reframe (public infrastructure rather than national-champion framing) is this blog's analysis, not a quoted project claim. The "open-weights lie, in three parts" decomposition is this blog's framing. The argument that the EU AI Act functions as a product specification for auditable model releases is this blog's original take, supported by the project materials but not claimed by the project. The "CSCS in Lugano" location detail is common knowledge about the Swiss National Supercomputing Centre and is not directly sourced from any of the cited Apertus documents. The disclosure explicitly flags every blog-original framing above.

Sources

  • Apertus project page — primary source for the release framing, model lineup, EU AI Act positioning, and links to all compliance documents: https://apertvs.ai/
  • Apertus technical documentation page — primary source for the model lineup (Apertus 8B / 70B / Mini 0.5B-4B / upcoming 1.5), Apache 2.0 license terms, EU Public Summary, EU Code of Practice, and supported runtimes (LM Studio, vLLM): https://apertvs.ai/pages/documentation/
  • Swiss AI Initiative home page — primary source for the 10,000+ GH200 Alps supercomputer, 10 million GPU-hour allocation, 20 million CHF ETH Domain grant, December 2023 start date, and 800+ researcher / 70+ AI professor headcount: https://swiss-ai.org/
  • swiss-ai/Apertus-70B-2509 Hugging Face model card — primary source for the 2 September 2025 release date, 32,804 all-time downloads, 154 likes, Apache 2.0 license, gating mechanism, usage-policy indemnification clause, six-monthly deletion-filter commitment, 1,000+ language coverage, long-context configuration, and the EU AI Act compliance artifacts: https://huggingface.co/swiss-ai/Apertus-70B-2509
  • swiss-ai/pretrain-data GitHub repository — primary source for the reconstruction-script approach to training-data transparency: https://github.com/swiss-ai/pretrain-data
  • arXiv:2509.14233, "Apertus: Democratizing Open and Compliant LLMs for Global Language Environments" (Project Apertus et al., submitted 17 September 2025, v2 1 December 2025) — primary source for the 70+ author consortium list, the training-pipeline details, the evaluation results, and the "democratizing open and compliant" framing: https://arxiv.org/abs/2509.14233
By Jiro at June 22, 2026
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