An encoder-based Decision 1.0 model built on the multilingual Vela encoder. It reads the context and candidate descriptions together and returns probabilities for Choice, Noul and Score questions. Inputs are limited to 1,024 tokens.
A niche 0.6B-parameter dense decision model from vLLM Semantic Router. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing. Newly released, so production-readiness is still being shaken out.
Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.
Access model weights, configuration files, and documentation.
The top devices for this model at 4-bit, ranked by fit and speed.
| Device | Grade | VRAM |
|---|---|---|
| ACEMAGIC M1A Pro (i9-13900HK + ARC A770)ACEMAGIC | SS | 0.9 GB |
| Acer Veriton GN100 AI MiniAcer | SS | 0.9 GB |
| AMD Instinct MI300XAMD | SS | 0.9 GB |
| AMD Instinct MI325XAMD | SS | 0.9 GB |
| AMD Instinct MI355XAMD | SS | 0.9 GB |

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| Option | Cost / GPU-hour |
|---|---|
NVIDIA GeForce RTX 4060Vast.ai · Spot · 8 GB VRAM | $0.03 |
NVIDIA GeForce RTX 2080 TiVast.ai · Spot · 11 GB VRAM | $0.04 |
NVIDIA GeForce RTX 3070Vast.ai · Spot · 8 GB VRAM | $0.05 |
NVIDIA GeForce RTX 3060Vast.ai · Spot · 12 GB VRAM | $0.05 |
NVIDIA GeForce RTX 4060 TiVast.ai · Spot · 8 GB VRAM | $0.05 |
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