Can I run FLUX.1 Dev on NVIDIA RTX 4070 Ti?

cancel
Fail/OOM
This GPU doesn't have enough VRAM
GPU VRAM
12.0GB
Required
24.0GB
Headroom
-12.0GB

VRAM Usage

0GB 100% used 12.0GB

info Technical Analysis

The primary limiting factor for running the FLUX.1 Dev model (12B parameters) on an NVIDIA RTX 4070 Ti is the GPU's VRAM capacity. FLUX.1 Dev, when using FP16 (half-precision floating point), requires approximately 24GB of VRAM to load and operate efficiently. The RTX 4070 Ti is equipped with 12GB of GDDR6X memory. This 12GB deficit prevents the model from being loaded entirely onto the GPU, leading to a compatibility failure. While the RTX 4070 Ti boasts a memory bandwidth of 0.5 TB/s, 7680 CUDA cores, and 240 Tensor cores based on the Ada Lovelace architecture, these specifications become irrelevant if the model cannot reside in the GPU's memory.

lightbulb Recommendation

Due to the VRAM limitation, running FLUX.1 Dev on the RTX 4070 Ti in FP16 is not feasible without significant modifications. Consider using quantization techniques, such as 8-bit or even 4-bit quantization (e.g., using bitsandbytes or GPTQ), to reduce the model's memory footprint. This will significantly lower the VRAM requirement, potentially bringing it within the 4070 Ti's 12GB capacity. Alternatively, explore offloading some layers to system RAM, though this will drastically reduce inference speed. If possible, consider upgrading to a GPU with more VRAM, such as the RTX 3090 or RTX 4080, to run the model without these compromises.

tune Recommended Settings

Batch_Size
1 (adjust downwards if issues arise)
Context_Length
Reduce context length if still facing VRAM issues
Other_Settings
['Enable CUDA graph capture if using PyTorch', 'Use CPU offloading sparingly as a last resort', 'Monitor VRAM usage closely during inference']
Inference_Framework
llama.cpp or PyTorch with bitsandbytes
Quantization_Suggested
4-bit or 8-bit quantization (QLORA or GPTQ)

help Frequently Asked Questions

Is FLUX.1 Dev compatible with NVIDIA RTX 4070 Ti? expand_more
No, not without significant quantization or offloading due to insufficient VRAM.
What VRAM is needed for FLUX.1 Dev? expand_more
FLUX.1 Dev requires approximately 24GB of VRAM in FP16 precision.
How fast will FLUX.1 Dev run on NVIDIA RTX 4070 Ti? expand_more
Performance will be severely limited or non-existent without quantization or offloading. With quantization, expect significantly reduced token generation speed compared to running on a GPU with sufficient VRAM. Performance will vary greatly based on the chosen quantization level and settings.