All models › swiss-ai
Apertus-8B-Instruct-2509
swiss-ai · open license · on Arena since 13 Aug 2025
Download No Arena votes
Arena score—Not voted on Arena yet
Size8.1 Bparameters
4-bit download4.9 GBweights only, Q4_K_M
Max context64Ktokens, from the model config
Where to use it for free
- Download and run it at home: official weights · GGUF files for Ollama and LM Studio
No Arena votes yet
Arena has not voted on this model. It is listed because it is a popular small model on its lab's official Hugging Face page (436K downloads a month).
Speed on popular hardware
| Hardware | GPU memory | Fits? | Speed |
|---|---|---|---|
| RTX 3060 12 GB | 12 GB | Fits in GPU | 27 tok/s |
| RTX 4060 Ti 16 GB | 16 GB | Fits in GPU | 22 tok/s |
| RTX 3090 | 24 GB | Fits in GPU | 71 tok/s |
| RTX 4090 | 24 GB | Fits in GPU | 77 tok/s |
| RTX 5090 | 32 GB | Fits in GPU | 136 tok/s |
| RX 7900 XTX | 24 GB | Fits in GPU | 73 tok/s |
| 2 × RTX 3090 | 48 GB | Fits in GPU | 69 tok/s |
| 4 × RTX 3090 | 96 GB | Fits in GPU | 65 tok/s |
| Mac M4 Max 128 GB | 96 GB | Fits in GPU | 42 tok/s |
| Ryzen AI Max+ 395 128 GB | 96 GB | Fits in GPU | 19 tok/s |
| 8 × H100 SXM | 640 GB | Fits in GPU | 211 tok/s |
Estimate at 4-bit with 32K context, 64 GB of DDR5 for offload. Speed ≈ memory bandwidth × 70% ÷ data read per token; real numbers vary by about ±30%.
GPU memory needed
| Level | 8K context | 32K context | Quality vs full |
|---|---|---|---|
| Q3_K_M | 5.7 GB | 8.9 GB | +10.5% noticeable loss |
| Q4_K_M | 6.6 GB | 9.8 GB | +2.8% small loss |
| Q5_K_M | 7.4 GB | 11 GB | +0.9% very small loss |
| Q6_K | 8.3 GB | 12 GB | +0.3% near-lossless |
| Q8_0 | 10 GB | 14 GB | ≈ 0% same as full |
| F16 | 18 GB | 21 GB | ≈ 0% same as full |
Weights + context cache (from the model's architecture) + runtime. Quality: increase in perplexity against the full model, from llama.cpp tests, scaled by model size. Simulate your own setup.