Can I run FLUX.1 Dev on NVIDIA RTX A4000?

cancel
Fail/OOM
This GPU doesn't have enough VRAM
GPU VRAM
16.0GB
Required
24.0GB
Headroom
-8.0GB

VRAM Usage

0GB 100% used 16.0GB

info Technical Analysis

The NVIDIA RTX A4000, with its 16GB of GDDR6 VRAM, falls short of the 24GB VRAM requirement for the FLUX.1 Dev model when using FP16 precision. This means the entire model cannot be loaded onto the GPU simultaneously, leading to a compatibility failure. The A4000's memory bandwidth of 0.45 TB/s, while respectable, would also become a bottleneck if workarounds like offloading layers to system RAM were attempted, severely impacting performance. Even if the model could be forced to run, the limited VRAM would necessitate extremely small batch sizes, resulting in unacceptably low throughput.

lightbulb Recommendation

Given the VRAM limitations, running FLUX.1 Dev on the RTX A4000 in FP16 is impractical. Consider exploring quantization techniques such as 8-bit or even 4-bit quantization to reduce the model's memory footprint. Alternatively, you could investigate using a different model with a smaller parameter size that fits within the A4000's VRAM. If feasible, upgrading to a GPU with at least 24GB of VRAM is the most straightforward solution for running FLUX.1 Dev without significant performance compromises.

tune Recommended Settings

Batch_Size
1
Context_Length
64
Other_Settings
['Use --threads to maximize CPU utilization during offloading.', 'Enable memory mapping (--mlock) if system RAM is sufficient.']
Inference_Framework
llama.cpp
Quantization_Suggested
Q4_K_M

help Frequently Asked Questions

Is FLUX.1 Dev compatible with NVIDIA RTX A4000? expand_more
No, the NVIDIA RTX A4000 does not have enough VRAM to load the FLUX.1 Dev model in FP16.
What VRAM is needed for FLUX.1 Dev? expand_more
FLUX.1 Dev requires approximately 24GB of VRAM when using FP16 precision.
How fast will FLUX.1 Dev run on NVIDIA RTX A4000? expand_more
Due to insufficient VRAM, the model is unlikely to run at an acceptable speed. Extreme quantization and CPU offloading would be necessary, resulting in very low tokens/second.