Models / GLiNER2.5-Decide

A decision model that fits in a phone, already ported to one. GLiNER2.5-Decide is Fastino’s 340M-parameter open-weight classifier: hand it text plus a typed schema of questions (intent, routing, sentiment, priority, multi-label tags, ordinal scores, a question over a passage) and it hands back structured answers with confidence scores in a single forward pass - no prompt template, no generated tokens. 29,199 downloads in its first week. Apache 2.0, GLiNER2 architecture, 1.95 GB in fp16.

Benchmarks, labeled. On Fastino’s own fast-decisions suite (17 domains, 300 held-out examples each) it scores 60.2% average exact-match, ahead of JevK5 (57.6%), SemIf at Qwen3.5-4B scale (56.4%), and Laya Router (46.6%). Treat the suite as vendor-run: it is their dataset, their split, their comparison. What the number does establish is the schema trick - one model answering whatever fixed question set you pass at call time, instead of one classifier per job.

What runs where. This is the CPU end of the decision-model map: 167 ms per call on a 48-core server CPU with no GPU at all, and a 1.95 GB fp16 footprint. The port ecosystem moved fast - an official LiteRT build landed two days after release for phone GPUs, and a community CoreML port exists for Apple devices. A multi sibling (287M) covers non-English input, and a 1B variant trades a little memory for 59.6% on the same suite.

What it is not. Not a reasoner and not a writer: no explanations, no open questions, no generated text - the card itself says the model is a specialist for operational decisions, and outputs below a threshold get dropped rather than guessed. Anything needing judgment or synthesis stays with the reasoning model.

Where it sits. Jev made typed decisions credible at flagship scale, AutoJev reproduced it open at 27B, Jeff fine-tunes it home at 2B, and GLiNER2.5-Decide is the fixed-schema industrial end - a model that never writes, answers exactly the questions you give it, runs on the hardware a helpdesk already owns, and costs the electricity of a lightbulb.

classification routing gating decision-model edge cpu
Parameters
340M
License
apache 2.0
Developer
Fastino
Origin
🇺🇸 USA
Released
Sep 2026

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Run it locally

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

LITERT
0.7GB 1.0GB min 2.0GB rec
Quantized build
FP16
2.0GB 2.0GB min 4.0GB rec
Full quality, largest

The reference hardware

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 Single GTX 1080 Ti (11GB) reference rig - 11GB VRAM, 484 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 LITERT FP16
4x H100 80GB (320GB) fast 10379.1t/s fast 3779.3t/s
NVIDIA DGX Station 748GB fast 6196.5t/s fast 2256.3t/s
8x RTX 3090 rack (192GB) fast 5801.2t/s fast 2112.4t/s
4x RTX 5090 (128GB) fast 5552.1t/s fast 2021.7t/s
AMD Instinct MI300X (192GB) fast 4124.4t/s fast 1501.8t/s
4x RTX 4090 (96GB) fast 3123.0t/s fast 1137.2t/s
2x RTX 5090 (64GB) fast 2776.0t/s fast 1010.8t/s
2x RTX 3090 (48GB) fast 1450.3t/s fast 528.1t/s
Single RTX 5090 (32GB) fast 1388.0t/s fast 505.4t/s
RTX PRO 6000 Blackwell (96GB) fast 1388.0t/s fast 505.4t/s
Mac Studio M4 Ultra 192GB fast 922.7t/s fast 336.0t/s
Mac Studio M4 Ultra 512GB fast 922.7t/s fast 336.0t/s
Single RTX 4090 (24GB) fast 780.8t/s fast 284.3t/s
MacBook Pro M5 Max 128GB fast 518.8t/s fast 188.9t/s
Single GTX 1080 Ti (11GB) fast 374.9t/s fast 136.5t/s
Dual EPYC 9004 + 768GB DDR5-4800 fast 356.9t/s fast 130.0t/s
DGX Spark 128GB unified fast 211.5t/s fast 77.0t/s
Ryzen AI Max+ 395 128GB fast 198.3t/s fast 72.2t/s
Jetson AGX Orin 64GB fast 158.6t/s fast 57.8t/s
Epyc + 512GB DDR4-3200 + 2x RTX 3090 fast 158.6t/s fast 57.8t/s
Epyc + 512GB DDR4-2400 + 2x RTX 3090 fast 119.0t/s fast 43.3t/s
NVIDIA Jetson Orin NX 16GB fast 79.3t/s fast 28.9t/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

LITERT official
0.7GB dl 1.0GB min 2.0GB rec
REC RAM vs largest quant
FP16 official
2.0GB dl 2.0GB min 4.0GB rec
REC RAM vs largest quant

Or run it in the cloud

No per-token API provider pricing tracked for GLiNER2.5-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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