Running a large language model locally has stopped being the hard part. Download a GGUF file, point llama.cpp at it, and you have inference on your own hardware with no API bill and no data leaving the building. The friction has moved somewhere less glamorous: fitting the model to the GPU you actually have, serving it in a way the rest of your stack can talk to, and — once you have more than one agent — getting them to coordinate instead of stepping on each other. That is the tooling layer we have been building.
The first wall everyone hits is VRAM. A GGUF model has knobs — quantization, how many layers you offload to the GPU, context length — and the right settings depend entirely on the specific card in front of you. Guess too high and it spills into system RAM and crawls, or fails to load outright; guess too low and you leave capability on the table. TurboProvider, our llama.cpp control panel, auto-tunes those parameters to the available VRAM so the model loads at a sane configuration without a manual afternoon of trial and error. It is a small thing that removes a genuinely annoying, repetitive tax.
The second wall is integration. Every framework, agent library, and internal tool already speaks the OpenAI chat-completions dialect, so anything that does not is friction you pay on every single call. TurboProvider serves an OpenAI-compatible endpoint, which means your local model is a drop-in for code that was written against a hosted API. You change a base URL, not your application. It is pure-stdlib Python and MIT-licensed on purpose — the tooling that removes friction should not add a dependency tree or a license negotiation of its own.
The third wall only appears once local inference works and you point several agents at it: they idle. Agents without shared state re-derive the same context, duplicate each other's work, and have no way to hand a task off. Skynet Cluster is our answer — an MCP-native coordination server that gives a fleet of agents a shared task board, a message bus, and shared memory. Instead of a pile of isolated processes each guessing at the whole problem, you get workers that claim tasks, post results, and read what the others already figured out.
The shared task board is the piece that changes the behavior most. When one agent can see what is already claimed and what is still open, the fleet stops colliding and starts dividing labor. Shared memory means a fact discovered once does not have to be rediscovered by every other agent, which is where a lot of local-agent setups quietly burn their compute. Coordination, not raw model size, is usually what separates a demo from something that finishes real work.
The through-line across all three is that the model was never the bottleneck. Usable local AI is a tooling problem: auto-tune the model to the hardware, serve it through the interface everything already speaks, and give multiple agents a place to share state. We build these as open-source tools because this layer should be common infrastructure, not something every team rebuilds badly in private.