Apertus-8B-Instruct-2509
swiss-aiapache-2.0text

Apertus-8B-Instruct-2509

Updated Aug 20, 2026
Parameters
8.1B
Context
65,536
License
apache-2.0
Updated
Aug 20, 2026

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Runs on your 24 GB - best at FP16
4 of 4 quantizations fit Apertus-8B-Instruct-2509 with real runtime overhead.
Q4_K_M
5 GB
Q5_K_M
6 GB
Q8_0
9 GB
FP16
16 GB
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve swiss-ai/Apertus-8B-Instruct-2509

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

Apertus-8B-Instruct-2509 is a 8.1B-parameter apache-2.0 model from swiss-ai. At Q4_K_M it needs roughly 5 GB of VRAM, placing it in the 8–12 GB GPU hardware tier.

Popularity

Apertus-8B-Instruct-2509 has 672,941 downloads in the last month on HuggingFace and 487 likes.

Frequently asked

Quick answers to common questions

How much VRAM does Apertus-8B-Instruct-2509 need?

Apertus-8B-Instruct-2509 with 8.1B parameters needs approximately 5 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is Apertus-8B-Instruct-2509 better than other swiss-ai models?

Apertus-8B-Instruct-2509 has 8.1B parameters with 65,536 context - a strong choice for general use.

What license is Apertus-8B-Instruct-2509 under?

Apertus-8B-Instruct-2509 is released under the apache-2.0 license, making it suitable for most commercial and personal projects.

What hardware runs Apertus-8B-Instruct-2509 well?

With 8.1B parameters, Apertus-8B-Instruct-2509 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for Apertus-8B-Instruct-2509?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~6 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

How long can Apertus-8B-Instruct-2509's context window handle?

Apertus-8B-Instruct-2509 supports a 65,536-token context window - enough for most medium-length documents and conversations. Real-world usable context may vary by implementation.

What models compete with Apertus-8B-Instruct-2509?

Apertus-8B-Instruct-2509 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec

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