At Inceptron, we’re on a mission to make AI more efficient, accessible, and scalable—across any hardware, framework, or deployment environment. To do that, we’ve built a powerful optimization compiler that helps teams get the most out of their models, whether they’re training LLMs, deploying diffusion models, or running inference at the edge or cloud.

Our roots are in ML research, with experience from top AI labs and infrastructure companies. We know what it takes to turn models into production—and we’ve built the tooling to do it fast, lean, and reliably, from compression and quantization to kernel-level runtime synthesis.

Customers work with Inceptron to cut inference latency, compress and quantize models at their accuracy budgets, and optimize across GPUs, CPUs, and accelerators like AWS Inferentia or FPGAs. Our compiler automates passes like shift-based reparameterization, mixed-precision optimization, memory and cache tuning, and workload-specific search for end-to-end performance. We support a wide range of deployment setups—from hybrid clouds and VPCs to open-source model integrations.

From our base in Lund, Sweden, we work with AI teams around the world—from startups building bleeding-edge LLM applications to enterprises in fintech, healthcare, AI neoscalers, and big tech—where model performance, cost-efficiency, and iteration speed are critical.

What makes us different? We combine automated benchmarking, compile-time profiling, and accuracy-preserving or use-case-tuned optimization into a modular system that meets real-world demands. Whether you’re training, tuning, or scaling, we plug in exactly where you need us—with white-glove support and deep engineering expertise to back it.

If you're building AI systems and want them faster, cheaper, and more scalable, we’d love to talk.