GLiNER2.5-Decide
edgeA 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.
- 340M
- apache 2.0
- 🇺🇸 USA
- Sep 2026
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Per-quant memory needs and a static "can you run it?" reference - no rig entry required
The reference hardware
22 reference configs, drawn in-house. Scroll for more.
Can you run it? - reference rigs
| Rig | LITERT | FP16 |
|---|---|---|
| 4x H100 80GB (320GB) | fast 9306.5t/s | fast 3627.1t/s |
| NVIDIA DGX Station 748GB | fast 5556.1t/s | fast 2165.4t/s |
| 8x RTX 3090 rack (192GB) | fast 5201.6t/s | fast 2027.3t/s |
| 4x RTX 5090 (128GB) | fast 4978.3t/s | fast 1940.2t/s |
| AMD Instinct MI300X (192GB) | fast 3698.2t/s | fast 1441.3t/s |
| 4x RTX 4090 (96GB) | fast 2800.3t/s | fast 1091.4t/s |
| 2x RTX 5090 (64GB) | fast 2489.1t/s | fast 970.1t/s |
| 2x RTX 3090 (48GB) | fast 1300.4t/s | fast 506.8t/s |
| Single RTX 5090 (32GB) | fast 1244.6t/s | fast 485.1t/s |
| RTX PRO 6000 Blackwell (96GB) | fast 1244.6t/s | fast 485.1t/s |
| Mac Studio M4 Ultra 192GB | fast 827.4t/s | fast 322.5t/s |
| Mac Studio M4 Ultra 512GB | fast 827.4t/s | fast 322.5t/s |
| Single RTX 4090 (24GB) | fast 700.1t/s | fast 272.9t/s |
| MacBook Pro M5 Max 128GB | fast 465.2t/s | fast 181.3t/s |
| Single GTX 1080 Ti (11GB) | fast 336.2t/s | fast 131.0t/s |
| Dual EPYC 9004 + 768GB DDR5-4800 | fast 320.0t/s | fast 124.7t/s |
| DGX Spark 128GB unified | fast 189.6t/s | fast 73.9t/s |
| Ryzen AI Max+ 395 128GB | fast 177.8t/s | fast 69.3t/s |
| Jetson AGX Orin 64GB | fast 142.2t/s | fast 55.4t/s |
| Epyc + 512GB DDR4-3200 + 2x RTX 3090 | fast 142.2t/s | fast 55.4t/s |
| Epyc + 512GB DDR4-2400 + 2x RTX 3090 | fast 106.7t/s | fast 41.6t/s |
| NVIDIA Jetson Orin NX 16GB | fast 71.1t/s | fast 27.7t/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
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.
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.