Feature Updates for oneinfer-edge

Hardware checks. Compatibility scans. Model deployment. Copilot routing. Local hosting. Multi-cloud instances. Cloud failover. Used to take a day. Now under 10 minutes.

AI moves fast. Deployment doesn't. 40% of teams take more than a week to get a single model into production. Data scientists spend over a quarter of their working day on setup, not science.

That's not an AI problem. That's an infrastructure problem.

oneinfer-edge fixes it. Not by reinventing the stack. By orchestrating what already exists into one open source control plane.

- Multiple serving libraries. One scan.
Ollama, llama.cpp, vLLM, SGLang, TensorRT-LLM, PyTorch, Dynamo. Instead of manually testing each one against your model and hardware, oneinfer-edge evaluates all five simultaneously and tells you exactly which one to use, for local, cloud, or both. Hours of trial and error eliminated before a single deployment.

- Traffic control panel for agentic harnesses. Zero code changes.
You can now leverage locally deployed models through the existing agentic copilots like codex, kilocode, opencode and openclaw and more upcoming.

-Model, serving library and hardware compatibility. Before you deploy.
Wrong serving library for your hardware. Wrong runtime for your model. These failures usually show up mid-deployment. oneinfer-edge runs a full compatibility scan across your model, your serving libraries, and your local hardware upfront. Complete picture. No surprises.

- Model and hardware resource checks. Local and cloud.
Paste any HuggingFace model ID. oneinfer-edge computes model weights, KV cache, and serving library overhead together and tells you whether it fits your machine or which cloud instance makes sense when local is not enough. No wasted downloads. No failed runs.

- Cloud instances marketplace. One API for everything.
Spin up instances across any cloud provider from the same control plane using a single OpenAI-compatible API. No switching between platforms. No managing separate configurations per provider. One place to create, manage, and monitor, regardless of which cloud you choose.

- Hybrid routing. Local, cloud, or both. Optimised automatically.
Local handles volume. Cloud handles complexity. When local capacity is exceeded, traffic fails over automatically. Routine tasks stay local. Complex reasoning goes to the cloud only when needed. Inference already accounts for 80 to 90% of the lifetime cost of a production AI system. Intelligent routing alone cuts that by 30 to 60%. Local-first hybrid orchestration pushes further.

We are just getting started. More coming in the next few days. Stay tuned!!!

Repo: https://github.com/oneinfer/oneinfer-edge

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