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A decision method on a frozen Gemma-4-12B-it with no fine-tuning, served on stock vLLM. A small shim presents the options as letters, reads the model’s probability for each letter and applies one calibration temperature. It answers Choice, Score and Noul questions with up to 26 options.
A workable 11.96B-parameter dense decision model from blockbrain. 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.
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 | 9.0 GB |
| Acer Veriton GN100 AI MiniAcer | SS | 9.0 GB |
| AMD Instinct MI300XAMD | SS | 9.0 GB |
| AMD Instinct MI325XAMD | SS | 9.0 GB |
| AMD Instinct MI355XAMD | SS | 9.0 GB |
Cheapest current cloud rentals with at least 9 GB VRAM, refreshed hourly.
Advertising disclosure: we earn commissions when you shop through the links below.
| Option | Cost / GPU-hour |
|---|---|
NVIDIA Tesla V100 16GBVast.ai · Spot · 16 GB VRAM | $0.04 |
NVIDIA GeForce RTX 5060 TiVast.ai · Spot · 16 GB VRAM | $0.07 |
NVIDIA GeForce RTX 4070Vast.ai · Spot · 12 GB VRAM | $0.08 |
NVIDIA Tesla V100 16GBVast.ai · On-Demand · 16 GB VRAM | $0.08 |
NVIDIA RTX A4000Vast.ai · Spot · 16 GB VRAM | $0.08 |
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.
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