How much VRAM does FLUX.1 schnell need?

At BF16 with everything resident, FLUX.1 schnell peaks at about 34.82 GB. Offloading components one at a time brings that to 23.58 GB, and quantising the weights to 4-bit brings it to 12.17 GB.

What is the cheapest GPU that runs FLUX.1 schnell?

The GeForce GTX 1650 at around $100, running it at FP8 with sequential offload, taking about 27 min 16 s per generation.

FLUX · Diffusion transformer

FLUX.1 schnell

FLUX.1 schnell is a pipeline of 4 networks totalling 16.86 billion parameters, of which 11.891 billion do the actual generating. At 1024x1024 that is 4,096 latent tokens per denoising step.

Same size as FLUX.1 dev but distilled to four steps, so it costs the same memory and a seventh of the time.

Whole pipeline16.86B

Denoiser11.891B

Download31.4 GB

Default steps4

What the pipeline is made of

This is the part that separates a media model from a language model: these are separate networks, and whether they sit in memory together is your choice, not the model's.

Component	Parameters	Published as	On disk	What it does
transformer	11.891B	BF16	22.15 GB	Does the generating. Runs once per step, so it dominates both memory and time.
text_encoder	0.123B	BF16	0.23 GB	Turns your prompt into conditioning. Runs once, then can leave the GPU entirely.
text_encoder_2	4.762B	BF16	8.87 GB	Turns your prompt into conditioning. Runs once, then can leave the GPU entirely.
vae	0.084B	BF16	0.16 GB	Converts between pixels and the compressed latent space. Small, but its decode pass is a memory spike.

These figures were derived from the published file sizes, because the repository is gated.

Memory by precision

Everything resident, at the model's native output size, on a card large enough that precision is the only constraint.

Precision	Peak VRAM	Quality	What it costs you
BF16	34.82 GB	100%	How the weights are published. No loss, and the largest footprint.
FP8	19.2 GB	97%	Halves the denoiser with a small, usually invisible cost. Needs Ada or newer.
GGUF Q8_0	20.17 GB	98%	Works on any card, unlike FP8. Slightly slower than native precision.
GGUF Q5_K_M	14.68 GB	95%	A middle step when Q8 will not fit.
GGUF Q4_K_M	13.01 GB	91%	The usual way a 12B image model gets onto an 8 GB card. Detail softens.
NF4	12.17 GB	89%	Aggressive 4-bit. Fast to load, noticeably looser on fine detail.

Memory by arrangement

The other lever: the same weights at the same precision, moved around differently. On this model quantising is the stronger lever: the denoiser is 71% of the pipeline and stays resident whatever you rearrange.

Arrangement	Peak VRAM	Time	How it works
Everything resident	34.82 GB	5.1 s	All components stay on the GPU. Fastest, and needs the most memory.
Text encoder on CPU	23.58 GB	5.4 s	The prompt is encoded once on the processor, so the encoder never touches the GPU at all. Standard practice on video models, whose encoders are often larger than their denoisers.
Component offload	23.42 GB	5.7 s	One component on the GPU at a time. The text encoder runs, then makes way for the denoiser. Costs a few seconds per generation.
Sequential offload	5 GB	30 s	Weights stream layer by layer from system RAM. Runs almost anything on almost anything, and is many times slower.

What a bigger output costs

Memory climbs with the pixel count, and the VAE decode pass climbs with it.

Output	Latent tokens	Peak VRAM	Time
512 x 512	1,024	32.93 GB	1.0 s
768 x 768	2,304	33.72 GB	2.5 s
1024 x 1024	4,096	34.82 GB	5.1 s
1536 x 1536	9,216	37.97 GB	16 s
2048 x 2048	16,384	42.37 GB	39 s

Measured at BF16 with everything resident on an RTX 4090, so the columns compare with each other rather than with your machine.

Which hardware runs FLUX.1 schnell

118 of 118 consumer devices run it in some arrangement. Each row shows the fastest arrangement that fits on that device.

Device	Memory	Verdict	Arrangement	Peak	Time
RTX PRO 6000 Blackwell NVIDIA	96 GB	Comfortable	BF16 everything resident	34.82 GB	3.5 s
GeForce RTX 5090 NVIDIA	32 GB	Comfortable	GGUF Q8_0 everything resident	20.17 GB	4.1 s
RTX 6000 Ada Generation NVIDIA	48 GB	Comfortable	BF16 everything resident	34.82 GB	4.7 s
GeForce RTX 4090 NVIDIA	24 GB	Workable	GGUF Q8_0 everything resident	20.17 GB	5.1 s
GeForce RTX 5090 Laptop NVIDIA	24 GB	Workable	GGUF Q8_0 everything resident	20.17 GB	6.4 s
NVIDIA DGX Spark 128GB NVIDIA	128 GB	Workable	BF16 everything resident	34.82 GB	6.7 s
GeForce RTX 5080 NVIDIA	16 GB	Workable	GGUF Q5_K_M everything resident	14.68 GB	7.4 s
GeForce RTX 4080 SUPER NVIDIA	16 GB	Workable	GGUF Q5_K_M everything resident	14.68 GB	8.0 s
GeForce RTX 4090 Laptop NVIDIA	16 GB	Workable	GGUF Q5_K_M everything resident	14.68 GB	8.3 s
GeForce RTX 4080 NVIDIA	16 GB	Workable	GGUF Q5_K_M everything resident	14.68 GB	8.5 s
GeForce RTX 5080 Laptop NVIDIA	16 GB	Workable	GGUF Q5_K_M everything resident	14.68 GB	8.7 s
GeForce RTX 5070 Ti NVIDIA	16 GB	Workable	GGUF Q5_K_M everything resident	14.68 GB	9.3 s
GeForce RTX 4070 Ti SUPER NVIDIA	16 GB	Workable	GGUF Q5_K_M everything resident	14.68 GB	9.4 s
GeForce RTX 3090 Ti NVIDIA	24 GB	Workable	GGUF Q8_0 everything resident	20.17 GB	10 s
RTX A6000 NVIDIA	48 GB	Workable	BF16 everything resident	34.82 GB	11 s
GeForce RTX 4070 Ti NVIDIA	12 GB	Workable	GGUF Q5_K_M text encoder on cpu	9.31 GB	11 s
GeForce RTX 3090 NVIDIA	24 GB	Workable	GGUF Q8_0 everything resident	20.17 GB	12 s
GeForce RTX 4080 Laptop NVIDIA	12 GB	Workable	GGUF Q5_K_M text encoder on cpu	9.31 GB	12 s
GeForce RTX 4070 SUPER NVIDIA	12 GB	Workable	GGUF Q5_K_M text encoder on cpu	9.31 GB	12 s
GeForce RTX 3080 Ti NVIDIA	12 GB	Workable	GGUF Q5_K_M text encoder on cpu	9.31 GB	13 s
GeForce RTX 5070 NVIDIA	12 GB	Workable	GGUF Q5_K_M text encoder on cpu	9.31 GB	14 s
GeForce RTX 3080 12GB NVIDIA	12 GB	Workable	GGUF Q5_K_M text encoder on cpu	9.31 GB	15 s
GeForce RTX 3080 10GB NVIDIA	10 GB	Workable	GGUF Q4_K_M text encoder on cpu	8.12 GB	15 s
RTX A5000 NVIDIA	24 GB	Workable	GGUF Q8_0 everything resident	20.17 GB	15 s
GeForce RTX 4070 NVIDIA	12 GB	Workable	GGUF Q5_K_M text encoder on cpu	9.31 GB	15 s
GeForce RTX 2080 Ti NVIDIA	11 GB	Workable	GGUF Q5_K_M text encoder on cpu	9.31 GB	16 s
GeForce RTX 5060 Ti 16GB NVIDIA	16 GB	Workable	GGUF Q5_K_M everything resident	14.68 GB	17 s
GeForce RTX 4060 Ti 16GB NVIDIA	16 GB	Workable	GGUF Q5_K_M everything resident	14.68 GB	19 s
Jetson AGX Orin 32GB NVIDIA	32 GB	Workable	GGUF Q8_0 everything resident	20.17 GB	19 s
Jetson AGX Orin 64GB NVIDIA	64 GB	Workable	BF16 everything resident	34.82 GB	19 s
Radeon RX 7900 XTX AMD	24 GB	Workable	GGUF Q8_0 everything resident	20.17 GB	19 s
Radeon PRO W7900 AMD	48 GB	Workable	BF16 everything resident	34.82 GB	20 s
RTX A4000 NVIDIA	16 GB	Slow	GGUF Q5_K_M everything resident	14.68 GB	21 s
Radeon RX 7900 XT AMD	20 GB	Slow	FP8 everything resident	19.2 GB	23 s
Radeon RX 9070 XT AMD	16 GB	Slow	GGUF Q5_K_M everything resident	14.68 GB	24 s
Radeon RX 7900 GRE AMD	16 GB	Slow	GGUF Q5_K_M everything resident	14.68 GB	26 s
Radeon RX 9070 AMD	16 GB	Slow	GGUF Q5_K_M everything resident	14.68 GB	30 s
Radeon RX 7800 XT AMD	16 GB	Slow	GGUF Q5_K_M everything resident	14.68 GB	32 s
GeForce RTX 2060 12GB NVIDIA	12 GB	Slow	GGUF Q5_K_M text encoder on cpu	9.31 GB	33 s
GeForce RTX 3060 12GB NVIDIA	12 GB	Slow	GGUF Q5_K_M text encoder on cpu	9.31 GB	33 s
Radeon RX 7700 XT AMD	12 GB	Slow	GGUF Q5_K_M text encoder on cpu	9.31 GB	35 s
Radeon RX 6900 XT AMD	16 GB	Slow	GGUF Q5_K_M everything resident	14.68 GB	51 s
Radeon RX 7600 XT AMD	16 GB	Slow	GGUF Q5_K_M everything resident	14.68 GB	52 s
Ryzen AI Max+ 395 32GB AMD	32 GB	Slow	GGUF Q8_0 everything resident	20.17 GB	60 s
Ryzen AI Max+ 395 64GB AMD	64 GB	Slow	BF16 everything resident	34.82 GB	60 s

Source: black-forest-labs/FLUX.1-schnell . Downloaded 0.7 million times in the last month. See how we calculate, or browse every image model.