A mid-size Decision 1.0 model built on Qwen3.5-4B. It routes requests among 2 to 255 actions, checks conditions against supplied evidence and applies scoring rubrics, returning a probability for each answer. Validated by its authors on AMD GPUs.
A situational 4.66B-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.
Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.
Access model weights, configuration files, and documentation.
Benchmark data sourced from: Hugging Face, Arena.ai, Artificial Analysis.
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 | 3.8 GB |
| Acer Veriton GN100 AI MiniAcer | SS | 3.8 GB |
| AI TOP ATOM ATAGB10-9003Gigabyte | SS | 3.8 GB |
| AMD Instinct MI300XAMD | SS | 3.8 GB |
| AMD Instinct MI325XAMD | SS | 3.8 GB |

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| Option | Cost / GPU-hour |
|---|---|
NVIDIA GeForce RTX 3060Vast.ai · Spot · 12 GB VRAM | $0.04 |
NVIDIA GeForce RTX 3060Vast.ai · On-Demand · 12 GB VRAM | $0.04 |
NVIDIA GeForce RTX 3080 TiVast.ai · Spot · 12 GB VRAM | $0.04 |
NVIDIA GeForce RTX 5060 TiVast.ai · Spot · 16 GB VRAM | $0.06 |
NVIDIA GeForce RTX 4060 TiVast.ai · Spot · 8 GB VRAM | $0.07 |
Per-GPU rate across RunPod, the Vast.ai marketplace, DigitalOcean, and Vultr.
Spot tier is interruptible. Plan for restarts when comparing against on-demand prices.