Clef
enthusiastA decision model from the company that routes a fifth of the web. Cloudflare shipped Clef (27B) and Clef-flash (9B) on October 1, 2026, Apache 2.0, fine-tuned from the Qwen3.5 27B backbone. Both are decision models in the Jev sense: hand one a question with a fixed set of answers (which inbox does this email route to, is this request abusive, which of these five actions should the agent take) and it returns one answer with a confidence number instead of a paragraph. Clef adds an image input, so a screenshot of a dashboard is a legal question. In a week Clef collected 1,859 likes and Clef-flash 17,600 downloads on Hugging Face.
What the flash size buys. Clef-flash at 9B is the laptop-runnable end of the decision-model map: about 5.4GB in 4-bit, which is a comfortable fit on a 16GB machine with the OS still loaded. Clef at 27B wants more: about 15.7GB in 4-bit, still one-card territory but with a KV cache that matters at length - 256MB per 1,000 tokens means a full 262k context costs about 67GB of cache on top, so the honest serving statement is that the 27B card runs whole-document decisions at 32k context in about 24GB, and short-prompt routing anywhere Qwen3.5 27B already runs.
Why a CDN company ships models. Cloudflare runs Workers AI as a serving platform and the decision-model shape (fast, typed, cheap at scale) is the exact shape a router wants for abuse detection, request classification, and agent-tool gating at the edge. The open weights are the credibility move: any customer can read what the classifier does before trusting it with their traffic, which is the same trust argument Clefβs own blog makes.
- 27.4B
- 262k
- apache 2.0
- πΊπΈ USA
- Oct 2026
Related models
Guides covering Clef
Save your hardware and every model page answers the real question: will it run on your machine, and how fast?
Join free - save your rig βRun it locally
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 | 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 440.7t/s | fast 132.3t/s |
| NVIDIA DGX Station 748GB | fast 263.1t/s | fast 79.0t/s |
| 8x RTX 3090 rack (192GB) | fast 246.3t/s | fast 73.9t/s |
| 4x RTX 5090 (128GB) | fast 235.7t/s | fast 70.8t/s |
| AMD Instinct MI300X (192GB) | fast 175.1t/s | fast 52.6t/s |
| 4x RTX 4090 (96GB) | fast 132.6t/s | fast 39.8t/s |
| 2x RTX 5090 (64GB) | fast 117.9t/s | fast 35.4t/s |
| 2x RTX 3090 (48GB) | fast 61.6t/s | offload |
| Single RTX 5090 (32GB) | fast 58.9t/s | no -> cloud |
| RTX PRO 6000 Blackwell (96GB) | fast 58.9t/s | ok 17.7t/s |
| Mac Studio M4 Ultra 192GB | fast 39.2t/s | ok 11.8t/s |
| Mac Studio M4 Ultra 512GB | fast 39.2t/s | ok 11.8t/s |
| Single RTX 4090 (24GB) | fast 33.2t/s | no -> cloud |
| MacBook Pro M5 Max 128GB | fast 22.0t/s | slow 6.6t/s |
| Dual EPYC 9004 + 768GB DDR5-4800 | ok 15.2t/s | slow 4.6t/s |
| DGX Spark 128GB unified | ok 9.0t/s | slow 2.7t/s |
| Ryzen AI Max+ 395 128GB | ok 8.4t/s | slow 2.5t/s |
| Jetson AGX Orin 64GB | slow 6.7t/s | slow 2.0t/s |
| Epyc + 512GB DDR4-3200 + 2x RTX 3090 | slow 6.7t/s | slow 2.0t/s |
| Epyc + 512GB DDR4-2400 + 2x RTX 3090 | slow 5.1t/s | slow 1.5t/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 Clef 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.