FLUX.1 dev is Black Forest Labs' 12-billion-parameter image model, and unlike its closed cousins it runs locally. The practical question is not "can my GPU load it" — that depends entirely on which checkpoint format you pick. A 12 GB card and a 32 GB card can both generate images, just with very different quality and speed ceilings. Here is the verified memory math.
How much VRAM does FLUX.1 dev need?
The published figures, computed from the model's verified architecture: FLUX.1 dev is a 12.00B-parameter rectified-flow transformer (August 2024). At the popular Q4_K_M quantization the total requirement is 9.9 GB, and the fit page's method is transparent — weights = parameters × bits-per-weight, plus 10% loading overhead, plus a 2 GB activation overhead for 1024×1024 generation (latent buffers, VAE, text encoders partially offloadable). Because this is a diffusion transformer there is no KV cache, which is why the same 9.9 GB holds at long contexts. The live GPU fit lists, grouped by headroom, are maintained on the FLUX.1 dev fit page — that is where to check before buying hardware.
Which quantizations fit smaller cards?
The quantization ladder is the whole story for a 12 GB class card, because the jumps are large:
| Variant / quant | Total VRAM @1024×1024 | Example GPU that fits |
|---|---|---|
| NF4 / GGUF Q4_K_M | 9.9 GB | RTX 3060 12GB (minimum tier) |
| Q6_K | 12.9 GB | RTX 4060 Ti 16GB |
| Q8_0 | 16.0 GB | RTX 4080 16GB |
| FP8 | ~17 GB | RTX 3090 / 4090 24GB (comfortable) |
| FP16 | 28.4 GB | RTX 5090 32GB; A100 40GB |
Practical reading: Q4_K_M/NF4 is the entry point for 12 GB cards at full 1024×1024 resolution, and the fit page classifies those as the minimum tier with little headroom. Q6_K and Q8_0 move you to 16 GB cards, and FP16 is datacenter or 32 GB territory. FP8 sits in the middle: about 15 GB for weights alone and about 17 GB with activation overhead, which makes 16 GB the practical FP8 floor with encoder offload and 24 GB comfortable.
What speed can you expect?
Speed is best described qualitatively, because the fit page publishes capacity math, not benchmark timings. Generation time on FLUX.1 dev is bound by memory bandwidth rather than raw compute: the page notes that bandwidth, measured in GB/s, matters as much as capacity — and flags 16 GB cards at FP8 as the "fast, high-quality" tier, while minimum-tier 12 GB cards fit the model but leave little headroom. The honest expectation is that a 12 GB card at Q4 generates, a 16 GB card at Q8_0 is meaningfully faster, and a 24 GB card at FP8 is the fastest consumer configuration — but actual seconds per image depend on the pipeline, resolution, and step count, and numbers should not be quoted without measuring on your own card.
Best value GPUs for FLUX right now
Value splits cleanly along two lines. If you already own a 12 GB card (RTX 3060 12GB, RTX 4070), the fit page's answer is: it runs at Q4/NF4 — no upgrade required. If you are buying, 16 GB is the sweet spot: the fit page's comfortable tier (25%+ headroom) starts there, with the RTX 4060 Ti 16GB and RTX 4080 both listed, and 16 GB also unlocks FP8 with encoder offload. The 24 GB tier (RTX 3090, RTX 4090) is where FP8 becomes truly comfortable and where the fit page stops flagging headroom concerns entirely — which is exactly why the used used RTX 3090 remains one of the best budget cards for local AI in 2026. For perspective on how the same cards behave on LLM workloads, our Qwen 3 32B local GPU guide covers the VRAM math for a text model of similar weight.
Frequently asked questions
Can an 8 GB GPU run FLUX.1 dev?
Not comfortably. Q4 weights plus activation overhead need about 10 GB, so an 8 GB GPU must offload the text encoders to system RAM and use GGUF Q3/Q4 with reduced resolution — which degrades quality and adds minutes per image. The fit page's own FAQ says this explicitly.
Is 12 GB enough for FLUX.1 dev?
Yes, at Q4/NF4 quantization: 9.9 GB total at 1024×1024, and the fit page lists 12 GB cards (RTX 3060 12GB, RTX 4070) as the minimum tier — the model fits, but with little headroom for larger resolutions or heavier pipelines.
What does FP8 need?
About 15 GB for FP8 weights alone and about 17 GB with activation overhead at 1024×1024. A 16 GB GPU is the practical FP8 floor with encoder offload; a 24 GB GPU runs FP8 comfortably.
Can a Mac run FLUX.1 dev?
Yes. The fit page confirms Apple Silicon machines fit FLUX.1 dev at Q4_K_M, since macOS lets the GPU use about 75% of unified memory — and notes generation speed is bound by memory bandwidth, so the GB/s figure matters as much as capacity.
Sources and further reading
- FLUX.1 dev fit page on Compare AI Hardware (linked above) — quantization table computed from parameters × bits-per-weight plus 10% loading overhead plus 2 GB activation overhead for 1024×1024; source architecture: 12.00B-parameter rectified-flow transformer, HF model card (black-forest-labs/FLUX.1-dev); FP8/NF4/GGUF variants assumed per checkpoint format
- GPU fit lists computed live from CompareAIHardware's GPU database; Mac figures assume macOS exposes ~75% of unified memory
Disclosure: machine-learning.null.pictures and compareaihardware.com are operated by the same team. Links to compareaihardware.com are editorial recommendations, not paid placements.
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- Deep Learning (Adaptive Computation and Machine Learning series) — Structured books beat scattered tutorials when you are building fundamentals.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow — Structured books beat scattered tutorials when you are building fundamentals.
- Pattern Recognition and Machine Learning (Information Science and Statistics) — Structured books beat scattered tutorials when you are building fundamentals.
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