Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs
arXiv:2606.12280v1 Announce Type: new Abstract: Post-training quantization lets large text-to-image diffusion transformers run on consumer GPUs, yet the hardware-specific trade-offs are seldom measured directly. We quantize Ideogram 4.0 - a 9.3B flow-matching diffusion transformer (DiT), shipped as two separate-weight copies of a single-stream 34-layer backbone for classifier-free guidance and conditioned by a Qwen3-VL-8B encoder - for Ampere RTX 3090 GPUs, which lack FP8 tensor cores. Our INT8 W8A8 recipe (per-channel weights, per-token dynamic activations, SmoothQuant, and mixed-precision pr
The ongoing pressure to lower computational costs and increase accessibility for large AI models drives continuous innovation in quantization techniques, making this specific advancement timely.
This development enables high-quality large text-to-image models to run on more ubiquitous consumer-grade GPUs, broadening access and potential applications beyond specialized hardware.
The barrier to entry for running advanced AI models like Ideogram 4.0 is significantly lowered, accelerating experimentation and deployment on a wider range of hardware.
- · Consumer GPU owners
- · AI developers
- · AI startups
- · On-device AI applications
- · High-end data center GPU providers (marginal)
- · Cloud AI inference providers (marginal)
More widespread access to powerful generative AI models for individual users and smaller organizations.
Increased innovation in AI applications that require local or cost-effective inference capabilities.
Potential acceleration of the 'AI on every device' paradigm, shifting some compute burden away from centralized clouds.
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Read at arXiv cs.LG