The optimization layer for
generative inference.
Fractalyze Inference makes existing image, video and speech models faster and cheaper, without retraining.
~7× faster. No additional training.
We optimize the execution, not the model.
What makes Fractalyze Inference different
No retraining
Only the execution changes: caching, step schedule, attention, kernels. Model weights stay untouched, so the checkpoint you have already validated is the one that runs.
Runs on your existing stack
Recipes drop into SGLang, vLLM and ComfyUI on the GPUs you already operate. No migration to a proprietary serving platform.
Measured, not marketed
Every recipe is published on Inference Frontier with latency and quality loss side by side, on the same model and hardware as the baseline.
A recipe per workload
Techniques are combined per model, hardware and quality target instead of applying one fixed trick everywhere.
Optimization is a quality-cost tradeoff.
Inference Frontier compares optimization recipes by both performance and quality loss, under the same model and hardware.
Frequently asked questions
Does Fractalyze Inference require retraining or fine-tuning?
No. It changes how the model executes (caching, step schedule, attention, kernels), not the weights. Your existing checkpoint is what runs.
Which models and serving stacks are supported?
Any model: the techniques work at the execution level, so support is not tied to a model list. Measured results so far cover Qwen-Image 2.1, and the benchmark is updated as new models are added. Serving stacks: SGLang, vLLM and ComfyUI.
How is quality measured?
Every recipe is benchmarked against the baseline on the same model and hardware. Quality loss is measured with multiple metrics (for images, e.g. LPIPS) and additionally validated by human review. Results are published on Inference Frontier.
How do I get started?
Send one representative workload and a quality target through the contact form. We benchmark your current stack and come back with an optimization plan.
Running an image, video or speech model in production?
Give us one representative workload and a quality target. We'll benchmark your current stack and see how much GPU cost we can remove on your existing hardware.