Realizing Native INT8 Compute for Diffusion Transformers on Consumer GPUs: A Fused INT8 GEMM Kernel for Ideogram 4.0
arXiv:2606.14598v1 Announce Type: new Abstract: Post-training INT8 (W8A8) quantization of diffusion transformers is widely deployed as a speed optimization, yet on consumer Ampere GPUs it is frequently slower than the FP8 and NF4 alternatives it is meant to beat. We trace this to a software artifact: the production "INT8" forward quantizes weights and activations only to immediately dequantize them back to bf16 and run a bf16 matrix multiply, never engaging the GPU's INT8 tensor cores, so the hardware's compute advantage is left entirely unrealized. We close this gap with a single fused Triton
The continuous push for efficiency in AI inference, coupled with the realization that current INT8 implementations for diffusion transformers on consumer GPUs are suboptimal, makes this a timely and impactful development.
This development directly addresses a critical performance bottleneck in running large AI models like diffusion transformers, enabling faster and more energy-efficient AI inference on widely available hardware, which impacts the scalability and cost of AI deployment.
Optimized INT8 computation will now genuinely leverage GPU tensor cores for diffusion models, providing significant speedups and reducing the computational gap between different quantization methods.
- · NVIDIA
- · AI developers
- · Cloud providers
- · Consumer GPU manufacturers
- · Less optimized AI inference solutions
- · Users relying solely on FP8/NF4 for speed
Diffusion models will become faster and more cost-effective to run on consumer-grade hardware.
This efficiency gain could accelerate the adoption and deployment of powerful generative AI models in edge devices and personal computing.
Increased accessibility to advanced AI capabilities might foster more innovation and new applications in creative industries and AI-powered interfaces.
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Read at arXiv cs.LG