Models / GEV-26B-Decide

The Jev recipe on a Gemma spine, at open-source velocity. GEV-26B-Decide is AutoTrust’s decision model on Google’s Gemma 4 26B backbone (A4B: 4B of it active at inference): image or text in, one probability score per allowed action out, one forward pass. 903,866 downloads and 1,588 likes in under a week on Hugging Face, Apache 2.0.

The base-family bet. The decision-model wave’s first generation all tuned Qwen backbones; GEV ports the recipe to Gemma and the same team ships JEV-27B-VL on Qwen with vision, so the two base families can be compared on the same task set. For deployment the Gemma port matters commercially: Gemma’s license terms are the lightest of the major bases for self-hosting at fleet scale, and the A4B active count means per-token compute is a quarter of the parameter count, so serving cost tracks roughly 4B-class even though 25.8GB of weights stay resident in 4-bit.

What runs where. 14.8GB of q4 weights plus a KV cache of 240MB per 1,000 tokens (30 layers x 8 kv-heads x 256 dim): a 24GB card serves routing-scale contexts comfortably, and a 384GB unified cluster (the 512GB Ultra tier) holds the whole 262k context with the KV ledger closed.

decision-model classification routing
Parameters
25.8B
Context
262k
License
apache 2.0
Developer
AutoTrust
Origin
🇺🇸 USA
Released
Oct 2026

Guides covering GEV-26B-Decide

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
14.8GB 16.0GB min 24.0GB rec
Balanced - the usual local sweet spot
FP16
51.6GB 52.0GB min 64.0GB rec
Full quality, largest

The reference hardware

Schematic of the NVIDIA Jetson Orin NX 16GB reference rig - 16GB unified memory, 102 GB/s aggregate bandwidth
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

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

Can you run it? - reference rigs

Rig Q4_K_M FP16
NVIDIA Jetson Orin NX 16GB tight no -> cloud
Single GTX 1080 Ti (11GB) offload no -> cloud
4x H100 80GB (320GB) fast 467.6t/s fast 140.2t/s
NVIDIA DGX Station 748GB fast 279.2t/s fast 83.7t/s
8x RTX 3090 rack (192GB) fast 261.4t/s fast 78.4t/s
4x RTX 5090 (128GB) fast 250.2t/s fast 75.0t/s
AMD Instinct MI300X (192GB) fast 185.8t/s fast 55.7t/s
4x RTX 4090 (96GB) fast 140.7t/s fast 42.2t/s
2x RTX 5090 (64GB) fast 125.1t/s fast 37.5t/s
2x RTX 3090 (48GB) fast 65.3t/s offload
Single RTX 5090 (32GB) fast 62.5t/s offload
RTX PRO 6000 Blackwell (96GB) fast 62.5t/s ok 18.8t/s
Mac Studio M4 Ultra 192GB fast 41.6t/s ok 12.5t/s
Mac Studio M4 Ultra 512GB fast 41.6t/s ok 12.5t/s
Single RTX 4090 (24GB) fast 35.2t/s no -> cloud
MacBook Pro M5 Max 128GB fast 23.4t/s slow 7.0t/s
Dual EPYC 9004 + 768GB DDR5-4800 ok 16.1t/s slow 4.8t/s
DGX Spark 128GB unified ok 9.5t/s slow 2.9t/s
Ryzen AI Max+ 395 128GB ok 8.9t/s slow 2.7t/s
Jetson AGX Orin 64GB slow 7.2t/s slow 2.1t/s
Epyc + 512GB DDR4-3200 + 2x RTX 3090 slow 7.2t/s slow 2.1t/s
Epyc + 512GB DDR4-2400 + 2x RTX 3090 slow 5.4t/s slow 1.6t/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
14.8GB dl 16.0GB min 24.0GB rec
REC RAM vs largest quant
14.8GB q4 weights + KV 240MB/1k (30L x 8 kv x 256); full 262k ctx = ~63GB cache
FP16 official
51.6GB dl 52.0GB min 64.0GB rec
REC RAM vs largest quant
51.6GB fp16 weights + KV 240MB/1k

Or run it in the cloud

No per-token API provider pricing tracked for GEV-26B-Decide 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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