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llama-swap
llama-swap routes requests to local AI model servers and starts or stops them as different models are needed. An application connects to the proxy while its configuration defines the servers behind it.
It works with compatible servers such as llama.cpp and vLLM. The repository includes source, release binaries, container routes and a browser interface for trying models and viewing activity.
What it is
One connection to several model servers
A proxy and process manager for local AI servers with supported OpenAI- or Anthropic-style API endpoints. The selected model determines where a request goes.
Why it stands out
Choose what stays loaded
Its default is one model at a time. Groups or a matrix of allowed combinations can describe which models may run together when the machine has capacity.
Availability
Source and release binaries
The project publishes binaries for Linux, macOS, Windows and FreeBSD, alongside container and source-build routes. It declares MIT; see the repository for current terms.
Why it matters
What makes it useful
Someone using several local models may want each available to an application without leaving every server running. llama-swap can load the requested server and unload another according to the configured rules. The application can keep one proxy address while choosing among model IDs. The model files and the engines that run them remain part of the local setup.
What to know
Where it fits
llama-swap sits between an API client and the inference servers. Its YAML configuration identifies model commands and proxy destinations; those engines perform generation. The web interface adds a playground, load and unload controls, request inspection and logs. A compatible request format still needs a backend that supports the requested task.
Notable points
What stands out
Groups and matrix routing offer different ways to describe coexistence. Groups express rules such as large models swapping while smaller services remain resident. The matrix router instead lists allowed combinations and chooses which running models to evict when a new one is requested. Its eviction costs can favour keeping a slow-loading server resident. These are configured coexistence rules, so the operator still needs to establish which combinations fit the machine.
Before using
What to review
The selected model files, inference engine and hardware must work together. The configuration needs real executable commands and paths; a proxy installation alone does not supply that setup.
The unloading guide distinguishes idle time from shutdown time. A positive TTL unloads after inactivity; a later request needs the model to load again. Docker-backed commands also need the documented stop command so unloading stops the container rather than only its local client.
The configuration reference says an empty API-key list disables authorization checks on inference endpoints. It also documents request and response capture controls. Review access settings and capture behaviour when deciding who can reach the service and which prompts to send through it.
Check the original listing's documentation for current requirements, limitations, licensing and any relevant privacy or security considerations.
Reader fit
Who may find it relevant
Relevant to practical builders and local AI users comfortable configuring model servers. The routing and unloading guides are useful starting points if the question is which models should share memory, which should swap, and how long an idle server should remain loaded.
Editorial note
Why LifeHubber lists it
LifeHubber lists llama-swap because managing local models also involves understanding what happened when a request was made. Its browser interface combines request and response inspection with server logs and manual load and unload controls. For a builder connecting several backends, that gives them specific things to examine: the request sent, the response returned and the server activity when a setup behaves differently from what they expected.
Source links
Source materials
Reader note
About our listings
LifeHubber’s listings help you explore what’s available in AI and are not endorsements. We don’t independently verify every detail or guarantee that each listing is accurate, safe, actively maintained, or right for your needs.
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