Apertus 8B — VRAM, speed & local setup
Written by Jakub Rusinowski · Last updated
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.
VRAM and speed by quantization
Quoted against NVIDIA RTX 4090 (24 GB). Weights plus overhead only: this model's architecture is unpublished, so no KV cache is added.
| Quant | Memory | VRAM | Speed (est.) | Fit |
|---|---|---|---|---|
| Q2_K 2.63 bpw | 3.4 GB | ~154 tok/s | Fits | |
| Q3_K_M 3.41 bpw | 4.2 GB | ~133 tok/s | Fits | |
| Q4_K_M 4.83 bpw | 5.6 GB | ~107 tok/s | Fits | |
| Q5_K_M 5.67 bpw | 6.5 GB | ~95 tok/s | Fits | |
| Q6_K 6.56 bpw | 7.4 GB | ~86 tok/s | Fits | |
| Q8_0 8.50 bpw | 9.3 GB | ~70 tok/s | Fits | |
| F16 16.00 bpw | 16.8 GB | ~41 tok/s | Fits |
Black marker = usable memory on the NVIDIA RTX 4090 (24 GB). Estimates from the memory-bandwidth roofline on the methodology page. Apertus 8B VRAM calculator →
Get Apertus 8B running
The cheapest catalogued GPU that runs Apertus 8B is the Intel Arc B570 (10 GB).
or compare on Vast.ai from $0.35/hr (typical low · varies)
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How to run Apertus 8B
Install Ollama, then run:
ollama run apertusSpecifications
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.
- Parameters
- 8 Billion
- Context window
- 65,536
- Architecture
- Dense
- Provider
- EPFL / ETH Zurich / CSCS
- Licence
- Apache 2.0
- Practical deployment
- Laptop
- Specified at
- Q4_K_M
- System RAM
- 16 GB
- Record updated
- 2026-09-19
Commercial use permitted. No usage restrictions beyond attribution.
Quality and use cases
Scores as published by the model’s authors or an independent evaluator — quality, not throughput, and not measured by us.
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.