Apertus 8B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated September 19, 2026

Model library → Apertus → Apertus 8B

The small Apertus, covering more than 1,000 languages. Runs on an 8 GB card at Q4.

Apertus 8B needs about 6 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters8 Billion
Context window65,536
ArchitectureDense
ProviderEPFL / ETH Zurich / CSCS
LicenceApache 2.0
Specified atQ4_K_M
System RAM16 GB
Record updated2026-09-19

Corroborated — Two or more independent sources agree on these figures, but the model card itself was not retrieved. Treat the numbers as good rather than confirmed. Still unconfirmed: context, releaseDate, hfModelId.

Licence

Apache-2.0 — commercial use permitted. Commercial use permitted. No usage restrictions beyond attribution.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB). Weights plus framework overhead only — this model publishes no architecture we can read, so no KV cache is included. A real session needs more; the figure is a floor, not a target. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantBits/weightWeightsVRAM neededEst. speedFit on 24 GB
Q2_K2.632.6 GB3.4 GB~154 tok/s (est.)Fits comfortably
Q3_K_M3.413.4 GB4.2 GB~133 tok/s (est.)Fits comfortably
Q4_K_M4.834.8 GB5.6 GB~107 tok/s (est.)Fits comfortably
Q5_K_M5.675.7 GB6.5 GB~95 tok/s (est.)Fits comfortably
Q6_K6.566.6 GB7.4 GB~86 tok/s (est.)Fits comfortably
Q8_08.508.5 GB9.3 GB~70 tok/s (est.)Fits comfortably
F1616.0016 GB16.8 GB~41 tok/s (est.)Fits comfortably

Want to set your own context length and KV-cache quantization? Use the interactive VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs Apertus 8B is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
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How to Run Apertus 8B

Install Ollama, then run:

ollama run apertus

Weights on Hugging Face: swiss-ai/Apertus-8B.

Best for: multilingual, european languages, chat, low resource languages.

Other Apertus Sizes

Apertus 8B — Frequently Asked Questions

How much VRAM does Apertus 8B need?
About 6 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Apertus 8B run on an RTX 4090 (24 GB)?
Yes. Apertus 8B needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 107 tokens/sec.
How do I run Apertus 8B locally?
Install Ollama and run `ollama run apertus`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Apertus come in?
Apertus 8B (6 GB), Apertus 70B (43 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

← All Apertus models | VRAM calculator | Check your own hardware