Deploy language models to any edge hardware without becoming a compression expert.
The software layer that compresses, compiles, and deploys models post-training to heterogeneous devices automatically — so LLM inference becomes as ubiquitous as electricity.
Latency, cost, privacy, and connectivity constraints make cloud-dependent inference unviable for real-world edge deployments. The bottleneck isn't the model — it's the round-trip.
Cloud round-trips are unacceptable for robotics, real-time interaction, and latency-critical applications.
API call costs compound at scale. Edge inference eliminates per-request fees and bandwidth overhead.
Healthcare, defense, and industrial deployments cannot send sensitive data off-site for inference.
Remote sites, IoT devices, and mobile applications cannot depend on constant internet access.
Point the CLI at a trained model and a device target. Get back a deployable, optimized artifact. No manual quantization. No backend configuration. No shipping runtime code.
Hardware-adaptive compression tailored to the target device class — not one-size-fits-all export.
Automatic backend selection across llama.cpp, MLC-LLM, ONNX Runtime + execution providers, PyTorch Mobile.
Push optimized models and LoRA adapter deltas over-the-air with rollback safety and version control.
$ e deploy \ --model llama-3.1-8b.safetensors \ --target ios/cpu,android/gpu,linux-arm64 \ --quantize int4 [COMPRESS] Quantizing to int4 w/ groupsize=128 [COMPILE] llama.cpp + NEON (ios/cpu), MLC-LLM + Vulkan (android/gpu) [PACKAGE] 3.2GB → 890MB per target [DEPLOY] Artifacts ready for OTA push
Not a single-device SDK. A complete deployment and management layer for heterogeneous edge hardware at fleet scale.
One model in, optimized artifacts for every target out. Automatic backend selection across llama.cpp, MLC-LLM, ONNX Runtime + execution providers, PyTorch Mobile.
Developers specify hardware targets, not compression parameters.
Run inference locally by default. Escalate to cloud only when the local model signals low confidence on a per-request basis.
Minimize latency and API cost without hard-coding thresholds.
Deploy model updates and LoRA adapters over-the-air with version control. Roll back instantly if production metrics degrade.
Ship model changes with the same safety guarantees as code deploys.
Ship LoRA-style deltas instead of full weight files. Update model behavior across the fleet without re-deploying the entire runtime.
Fine-tuned model updates measured in megabytes, not gigabytes.
We don't sell hardware. We make your models run on whatever hardware your users already have — mobile, embedded, industrial, whatever. One CLI, any target.
No API keys, no metered usage
Sub-100ms decision latency
Factory-floor inference without cloud egress
HIPAA-compliant on-device inference
Air-gapped, classified workloads
Offline-first operation
Currently working with select partners to refine deployment tooling and runtime performance across target hardware classes.
Join the private beta. We're working with teams shipping LLM-powered products on embedded and mobile hardware.