Models / Torchcast Decision 12B

Torchcast Decision 12B

consumer

One more voice in the flood, with the strictest license of the wave. Torchcast Decision 12B is an 11.96B-parameter decision model on the Gemma 4 Unified backbone from torchcast-ai, released October 2, 2026: the same typed-answer contract as its peers (a fixed set of options, one pick, a confidence number), 6.8GB in 4-bit so it lands comfortably on a 16GB card.

The license is the decision. CC BY-NC 4.0 means non-commercial: research, evaluation, hobby rigs, and coursework can run it; anything a business charges for cannot, not without a separate commercial license. Every other open row in the October decision wave (Clef, pplx-decider, GEV, JEV-VL) shipped Apache 2.0, so this is the card whose fine print a deployment checklist catches. The community quantizers published GGUF builds anyway (9,647 downloads on mradermacher’s 12B quant within days); nobody shipping it into a product can keep them there.

Where it sits. Compared with its Apache siblings at 2-4x the size, the interesting question is whether the 12B class earns its memory at all: the honest use is as an evaluation baseline (does a twice-as-small Apache model match it on your routing set?) or as a personal-standalone classifier. Not the default recommendation for production routing.

decision-model classification
Parameters
12.0B
Context
262k
License
cc by nc 4.0
Developer
Torchcast
Origin
🇺🇸 USA
Released
Oct 2026

Guides covering Torchcast Decision 12B

Save your hardware and every model page answers the real question: will it run on your machine, and how fast?

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Run it locally

Per-quant memory needs and a static "can you run it?" reference - no rig entry required

Q4_K_M
6.8GB 8.0GB min 16.0GB rec
Balanced - the usual local sweet spot

The reference hardware

Schematic of the Single GTX 1080 Ti (11GB) reference rig - 11GB VRAM, 484 GB/s aggregate bandwidth
Schematic of the 4x H100 80GB (320GB) reference rig - 320GB VRAM, 13400 GB/s aggregate bandwidth
Schematic of the NVIDIA DGX Station 748GB reference rig - 748GB unified memory, 8000 GB/s aggregate bandwidth
Schematic of the 8x RTX 3090 rack (192GB) reference rig - 192GB VRAM, 7489 GB/s aggregate bandwidth
Schematic of the 4x RTX 5090 (128GB) reference rig - 128GB VRAM, 7168 GB/s aggregate bandwidth
Schematic of the AMD Instinct MI300X (192GB) reference rig - 192GB VRAM, 5324 GB/s aggregate bandwidth
Schematic of the 4x RTX 4090 (96GB) reference rig - 96GB VRAM, 4032 GB/s aggregate bandwidth
Schematic of the 2x RTX 5090 (64GB) reference rig - 64GB VRAM, 3584 GB/s aggregate bandwidth
Schematic of the 2x RTX 3090 (48GB) reference rig - 48GB VRAM, 1872 GB/s aggregate bandwidth
Schematic of the Single RTX 5090 (32GB) reference rig - 32GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the RTX PRO 6000 Blackwell (96GB) reference rig - 96GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 192GB reference rig - 192GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 512GB reference rig - 512GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Single RTX 4090 (24GB) reference rig - 24GB VRAM, 1008 GB/s aggregate bandwidth
Schematic of the MacBook Pro M5 Max 128GB reference rig - 128GB unified memory, 614 GB/s aggregate bandwidth
Schematic of the Dual EPYC 9004 + 768GB DDR5-4800 reference rig - 768GB unified memory, 460 GB/s aggregate bandwidth
Schematic of the DGX Spark 128GB unified reference rig - 128GB unified memory, 273 GB/s aggregate bandwidth
Schematic of the Ryzen AI Max+ 395 128GB reference rig - 128GB unified memory, 256 GB/s aggregate bandwidth
Schematic of the Jetson AGX Orin 64GB reference rig - 64GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-3200 + 2x RTX 3090 reference rig - 560GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-2400 + 2x RTX 3090 reference rig - 560GB unified memory, 153 GB/s aggregate bandwidth
Schematic of the NVIDIA Jetson Orin NX 16GB reference rig - 16GB unified memory, 102 GB/s aggregate bandwidth

22 reference configs, drawn in-house. Scroll for more.

Can you run it? - reference rigs

Rig Q4_K_M
Single GTX 1080 Ti (11GB) tight
4x H100 80GB (320GB) fast 884.1t/s
NVIDIA DGX Station 748GB fast 527.8t/s
8x RTX 3090 rack (192GB) fast 494.2t/s
4x RTX 5090 (128GB) fast 472.9t/s
AMD Instinct MI300X (192GB) fast 351.3t/s
4x RTX 4090 (96GB) fast 266.0t/s
2x RTX 5090 (64GB) fast 236.5t/s
2x RTX 3090 (48GB) fast 123.5t/s
Single RTX 5090 (32GB) fast 118.2t/s
RTX PRO 6000 Blackwell (96GB) fast 118.2t/s
Mac Studio M4 Ultra 192GB fast 78.6t/s
Mac Studio M4 Ultra 512GB fast 78.6t/s
Single RTX 4090 (24GB) fast 66.5t/s
MacBook Pro M5 Max 128GB fast 44.2t/s
Dual EPYC 9004 + 768GB DDR5-4800 fast 30.4t/s
DGX Spark 128GB unified ok 18.0t/s
Ryzen AI Max+ 395 128GB ok 16.9t/s
Jetson AGX Orin 64GB ok 13.5t/s
Epyc + 512GB DDR4-3200 + 2x RTX 3090 ok 13.5t/s
Epyc + 512GB DDR4-2400 + 2x RTX 3090 ok 10.1t/s
NVIDIA Jetson Orin NX 16GB slow 6.8t/s

Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast >=20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark.

Download options

Q4_K_M community
6.8GB dl 8.0GB min 16.0GB rec
REC RAM vs largest quant
6.8GB q4 weights + KV 384MB/1k (48L x 8 kv x 256: the largest cache of the wave); full ctx wants 107GB, routing ctx fits 16GB

Or run it in the cloud

No per-token API provider pricing tracked for Torchcast Decision 12B yet. For flagship list prices, see the calculator.

PRICE HISTORY

Inference cost over time

Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.

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