FLUX.2 [klein] 4B
A solid pick for self-hosting and full data control.
Overview
FLUX.2 [klein] 4B is a text-to-image and editing model from Black Forest Labs (Germany), built for interactive, latency-critical use: sub-second inference on a single consumer GPU with as little as 13GB VRAM. It unifies text-to-image generation and multi-reference editing in one compact, Apache 2.0 licensed model cleared for commercial use. A real limit: text rendered inside images stays inaccurate, and output quality is highly sensitive to prompt phrasing. Open weights from a European vendor make fully local, on-prem deployment straightforward, with no data leaving the machine.
Skill profile
Not disclosed
Three independent measures
What people prefer, what the model can do, what the market uses. Each source has its biases and none is enough on its own: we show them side by side, never blended.
Strengths
- Open-source and self-hostable
Limitations
- API pricing not disclosed
Who is it for
- you want to control cost or self-host
- your data must stay in the EU
Ideal use cases
- Local image generation
- Fast multi-reference editing
- Edge creative prototyping
- Embedded product pipelines
- Secure on-prem deployment
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
19/19 criteria documented · 100 % · 12 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
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[![nAIvigate Trust Index · FLUX.2 [klein] 4B](https://naivigate.com/badge/flux-2-klein-4b.svg?lang=en)](https://naivigate.com/en/model/flux-2-klein-4b)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Yes |
| Data processing agreement (DPA) | N/A· self-hosted: data never leaves |
| Transfer outside the EU | No |
| Training on your data | No |
| Configurable retention | N/A· self-hosted: data never leaves |
| Zero Data Retention | N/A· self-hosted: data never leaves |
| Self-hostable | Yes |
| Declared hosting | self |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
4/8 documented · 1/8 primary sources
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 2.3 GB | 5 GB | Any recent PC/Mac |
| Q8 · balanced | 4.3 GB | 7 GB | 16GB PC / M1+ Mac / 8GB GPU |
| FP16 · max quality | 8 GB | 11 GB | 32GB PC / 24GB Mac / RTX 4070 Ti+ |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve black-forest-labs/FLUX.2-klein-4B \
--max-model-len 32768 \
--tensor-parallel-size 1 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | — |
| MMLU | — |
| GPQA | — |
| HumanEval | — |
| SWE-Bench | — |
| MATH | — |