Inference for Edge Devices
Capable agents on device, for the first time
Edge inference fails on memory. Our runtime compresses the KV cache as an agent runs, so devices can power agents that were previously impossible.
SUBCONSCIOUS
OUT OF MEMORY
GENERIC RUNTIME
Illustrative. The KV cache grows during an agent run. Compressing it at runtime allows a long-running agent to fit on device.
Run your agents anywhere
The devices we've worked with
Workstations
Teams with shared compute workstations.
Laptops
Laptops running Linux, Windows, or MacOS.
Mobile Devices
Phones and tablets running iOS or Android.
Edge Devices
Sensors, smart glasses, and other edge hardware.
The models
Run the best SLMs on our edge inference system
Qwen
Qwen3.5-2B and 4B run on phones and tablets. Best for multilingual chat, coding, and on-device tool use.
Gemma
Gemma 3n E2B and Gemma 3 4B are mobile-first and multimodal. Best for text, image, and audio tasks on device.
Nemotron
Nemotron 3 Nano is a tiny mixture-of-experts model. Best for agentic tool use, planning, and multi-step reasoning.
Deepseek
DeepSeek-R1, from 1.5B to 8B, brings chain-of-thought to a laptop. Best for math, logic, and structured reasoning.
And more. Run other open models, or bring your own.
Post-training
Get frontier performance on your use case
Have a unique workload you want to run on the edge? Post-train a model with Redline, our training system, and get a tiny model that hits frontier performance on the tasks you care about.
Talk to Us About Post-Training →Extremely efficient
Train on long reasoning trajectories with a fraction of the usual compute.
Long-horizon by design
Models learn from full reasoning traces, not just final answers.
Expert support
Our team of AI researchers scopes and trains the model with you.
Licensing
Run models on your devices
Make use of the edge GPUs you already own