Apertus 8B
The compact Swiss AI release trained on the Alps supercomputer, covering 1000+ languages including Swiss German and Romansh. Apache 2.0.
By Swiss AI · Switzerland
Updated 2026-09-15
When to pick this model
- Local multilingual EU deployments
- On-device assistants for French, German, Italian, or Romansh
- Data-sovereignty-sensitive prototypes
- Apache-licensed baseline for European fine-tuning
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 6 GB |
| Q5_K_M | 7 GB |
| Q8_0 | 10 GB |
| FP16 (no quantization) | 16 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Apertus 8B fits an 8 GB consumer card at Q4_K_M (6 GB). Stepping up to Q8_0 raises the footprint to 10 GB, and unquantized FP16 weights take 16 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Apertus 8B needs roughly 10 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 10 tokens/sec on entry-level GPUs, on the order of 30 tokens/sec on a mid-range card, and up to 80 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Apertus 8B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Apertus 8B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q5_K_M (7 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (10 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (16 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (16 GB used) |
| 32 GB | RTX 5090 | FP16 (16 GB used) |
Which hardware should you buy to run Apertus 8B?
To run Apertus 8B locally at Q4, you need ~6 GB for Q4 weights alone. Hardware option to compare: RTX 5060 Ti 16GB (ASUS Prime). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.
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Strengths
- Around 6 GB VRAM at Q4 — runs on consumer hardware
- Native EU multilingual coverage
- Apache 2.0 license
- Practical for everyday assistant use
Limitations
- Trails Qwen 3 8B on English and coding tasks
- Limited public fine-tunes
- Less benchmark coverage than mainstream 8B models
Typical workloads
In our catalog grid, Apertus 8B is filed under Compact Sovereignty, Laptop Multilingual — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads; French-language output where quality matters.
The 64k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: Dense · 8B · Swiss AI Initiative · compact multilingual EU
Training: Swiss AI — compact version of the sovereign European model.
The accessible sovereign 8B for European multilingual work — choose it when language reach beats benchmark dominance.
Quick start
Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.
Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.
ollama pull hf.co/swissai/Apertus-8B-GGUFOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Apertus 8B need?
At the recommended Q4_K_M quantization, Apertus 8B needs about 6 GB of VRAM. Q8_0 takes 10 GB, and unquantized FP16 weights take 16 GB.
Can Apertus 8B run without a GPU?
Yes — with roughly 10 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Apertus 8B support?
Apertus 8B supports a 64k-token context window (65,536 tokens).
Can I use Apertus 8B commercially?
Yes. Apertus 8B is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Apertus 8B on consumer hardware?
Our compatibility engine estimates on the order of 30 tokens/sec on a mid-range GPU and up to 80 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Apertus 8B should I download first?
Start with Q4_K_M (6 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.