Apertus 1.5 8B
A solid pick for self-hosting and full data control.
Overview
Apertus 1.5 8B is the small version of Apertus 1.5, released on 24 July 2026 with 4 trillion additional training tokens. It shares the 70B's new features: image and audio understanding, an optional reasoning mode and a 262,144-token context. Weights, data, recipes and intermediate checkpoints are published under Apache 2.0, with an acceptable use policy to accept at download. Its size makes it usable on a workstation or a modest server, an option for organisations that want an end-to-end traceable model without heavy infrastructure. Hugging Face lists it at around 9 billion parameters because of enlarged embedding layers.
Skill profile
Not disclosed
Three independent measures
What people prefer, what the model can do, what the market uses. Each source has its biases and none is enough on its own: we show them side by side, never blended.
Strengths
- Open-source and self-hostable
Limitations
- No documented EU residency
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- you handle sensitive data with no EU option
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
13/19 criteria documented · 68 % · 7 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
[](https://naivigate.com/en/model/apertus-1-5-8b)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Not documented |
| Data processing agreement (DPA) | Not documented |
| Transfer outside the EU | Not documented |
| Training on your data | Not documented |
| Configurable retention | Not documented |
| Zero Data Retention | Not documented |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
4/8 documented · 2/8 primary sources
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 5.1 GB | 8 GB | 16GB PC / M1+ Mac / 8GB GPU |
| Q8 · balanced | 9.6 GB | 13 GB | 32GB PC / 24GB Mac / RTX 4070 Ti+ |
| FP16 · max quality | 18 GB | 22 GB | 32GB Mac / RTX 3090-4090 |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve swiss-ai/Apertus-v1.5-8B \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |