Built for many requests on many machines.
Most local model tools are built around one machine and one user. MirbadLLM starts from the opposite end: several machines you own, and several people and agents asking at the same time.
What MirbadLLM is built around
Concurrency
Parallel coding agents in VS Code, a small team or a family on one endpoint at the same time. Models are spread across machines, and models on vLLM serve many requests at once.
The right model for each job
Each machine serves the models that fit it, so every tool gets a model chosen for its task instead of whatever fits on one box.
Safe on shared machines
Memory is checked before every start, per GPU, and a start that would not fit is refused with the reason.
Measured on your hardware
Benchmarks at 1, 16 and the maximum number of parallel requests, and a tuner that changes nothing until you press Apply.
Side by side
| Project | What it is for | Where MirbadLLM differs |
|---|---|---|
| Ollama | Running models on one machine, simply, with a CLI and a local API. | Each Ollama install serves the machine it runs on. MirbadLLM manages several machines from one place, behind one endpoint, and can list an existing Ollama as a host service next to its models. |
| LM Studio | A desktop app to download, run and chat with models on one machine. | Headless nodes, a fleet view, API keys per person and tool, scheduled backups. |
| GPUStack | Managing GPU clusters for model serving. | The closest neighbour. MirbadLLM aims at a handful of spare home and office machines, with memory checks per GPU before every start, a benchmark and tuner, and backups of the whole setup. |
| exo | Splitting one model across several devices to run models larger than any one of them. | MirbadLLM places whole models on the machines that fit them, so each machine answers its own requests in parallel. |
| LiteLLM | A gateway in front of many model APIs, local and hosted. | MirbadLLM also downloads, places, starts and watches the models, not only routes to them. The two can work together. |
When to pick something else
One machine, one person
Ollama or LM Studio is simpler, and that is fine.
One model too big for any machine you have
exo splits a model across devices; MirbadLLM does not.
Mostly hosted APIs
A gateway such as LiteLLM fits better.
AMD GPUs or CPU-only machines
Not supported by MirbadLLM yet.