Your models, served faster.
Multimodal models push GPUs hard. We make yours faster with distillation and inference optimization, then run them on our infrastructure, so you don’t need your own serving stack.
Hedra is a research and infrastructure company bringing frontier inference to multimodal and physical AI. We serve your models and keep improving them.
Multimodal models push GPUs hard. We make yours faster with distillation and inference optimization, then run them on our infrastructure, so you don’t need your own serving stack.
General-purpose models fall short on real-world problems. Our forward deployed researchers join your team, learn the problem and your hardware, and build your model with you.
Our researchers set the goals and guardrails. Agents run the experiments that keep improving your model. That is recursive self-improvement as a service.
Evidence · Krea 3 on AMD Instinct MI355X
22.9 s → 3.3 s
For Krea 3, our researchers tuned and then custom-implemented parts of the inference stack on AMD Instinct MI355X GPUs, using a quality check against a BF16 reference to decide which lower-precision settings to keep.
Median DiT time per edit fell from 22.9 s to 3.3 s for a 1 MP reference and 4 MP output on AMD Instinct MI355X GPUs, combined with model-level optimizations Krea provided. This measures DiT time, not full-request latency.
Read the engineering write-upBefore · 22.9 s
After · 3.3 s
1 MP reference · 4 MP output
Managed model APIs or optimized serving for your own model.
Post-train models with SFT and RL.
Isolated GPU environments for your own code.
An inference engine for image, video and world models.
Where models run, who operates them and what each option requires.
Compare deployment optionsFor workloads with jurisdiction, residency or control requirements.
Explore sovereign deploymentDeployment, security, ownership and commercial terms in one place.
Working with HedraWhat researchers decide, what agents run, and how accepted results change the next cycle.
RSI researchHow generative models represent distinct concepts, and how to steer one without disturbing the rest.
Interpretability researchMethods and evaluations for adapting multimodal models with SFT and reinforcement learning.
Post-training researchMaking large multimodal models fast and efficient on real hardware, with measured conditions.
Inference research