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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11If your priority is a documented way to unload a local model when idle and reload it for the next request, llama.cpp server is the clearest match in the reviewed documentation. Among alternatives, Ollama stands out for simple, explicit keep-warm controls: set how long a model stays loaded, keep it loaded indefinitely, or unload it immediately after a response. The right choice depends on whether you want automatic sleep, per-request retention, or on-demand routing across several models.
What does reliable idle handling mean?
For a local LLM server, idle handling is the policy for keeping a model loaded in memory, unloading it after inactivity, and making it available again when a request arrives. Keeping it loaded can avoid the model-loading work on a later request, but retains memory. Unloading releases model memory; a later request must trigger loading before inference can resume. The documentation establishes those lifecycle behaviors, not a guaranteed wake-up time.
Look for controls that are explicit and observable, and distinguish a whole-model unload from process health checks, model availability, or unloading a lightweight adapter. Actual memory use and request behavior depend on the model, context length, concurrency, hardware, memory budget, and retention setting.
Which alternatives document idle controls?
| Server | What its documentation establishes | Idle handling | Best fit |
|---|---|---|---|
| llama.cpp server (llama-server) | HTTP server with OpenAI-compatible routes, health checks, and optional model-router mode. | --sleep-idle-seconds sets an inactivity threshold; the default, -1, disables sleep. Sleep unloads the model and associated memory, including the KV cache. A new task triggers reload. |
Explicit automatic idle sleep, or on-demand routing among model instances. |
| Ollama | Local model server with server-wide and per-request retention controls. | Models stay loaded for five minutes by default. keep_alive accepts a duration or seconds, a negative value for indefinite retention, or 0 to unload after a response. |
Simple keep-warm policy or immediate post-response unload. |
| LM Studio | Local and network API serving; llama.cpp runtimes on Mac, Windows, and Linux; MLX on Apple Silicon; and headless operation with llmster. | Automatic whole-model idle-unload behavior is not established by the cited documentation. | Desktop model management or headless API serving when its runtime and workflow fit. |
| LocalAI | One client-facing API with selectable backends, including llama.cpp, vLLM, SGLang, and MLX. | Automatic whole-model idle-unload behavior is not established by the cited documentation. | Backend flexibility behind a common API. |
| vLLM | HTTP serving with OpenAI-compatible endpoints and other API families. | General whole-model idle unloading is not established. Documented LoRA adapter load/unload routes are for local development and do not demonstrate base-model unloading. | When its serving interface and deployment needs fit; validate model lifecycle separately. |
Documentation and command-line options can change. In particular, the llama.cpp README describes the project’s rolling master branch; pin a release or commit when you need reproducible deployment instructions.
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How do I keep a model loaded in memory or make it unload immediately?
Ollama: choose a retention period
The Ollama FAQ says models are kept in memory for five minutes by default before being unloaded. For /api/generate and /api/chat, pass keep_alive to set a duration string or a number of seconds. Use a negative value to keep the model loaded indefinitely, or 0 to unload it after generating a response. The request value overrides the server-wide OLLAMA_KEEP_ALIVE default.
For an immediate stop outside a generation request, the FAQ documents ollama stop <model>. This is distinct from setting the duration for future requests.
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llama.cpp: sleep after inactivity
Start llama-server with --sleep-idle-seconds SECONDS to set the idle threshold. The documented default is -1, which disables idle sleep. When the threshold is reached, the server unloads the model and associated memory, including the KV cache; a new task triggers model reload. The README describes this behavior as conserving resources.
To inspect sleep state, query GET /props. The README says requests to /health, /props, /models, and /metrics do not count as incoming work, reset the idle timer, or trigger a model reload. Monitoring can therefore poll these endpoints without keeping the model warm.
Rank #3
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When should I use a model router instead of idle sleep?
Idle sleep addresses one server’s retention of a model: keep it warm while active, then unload it after inactivity. A model router addresses a different need: serving a catalog and loading the selected model instance as requests arrive. The llama.cpp server documentation describes optional router mode for loading and unloading model instances on demand. That can suit a local endpoint serving several models, rather than one fixed model. Routing does not by itself establish a particular idle timeout or wake-up delay; configure and verify the lifecycle behavior you need.
How should I choose for a local deployment?
- Choose Ollama if you want a straightforward global default plus per-request control over how long a model stays loaded, including an immediate post-response unload option.
- Choose llama.cpp server if you want a documented inactivity-based sleep policy, visibility into sleep state, or optional on-demand model routing.
- Consider LM Studio if its local or network API workflow, supported runtime, model management, or headless mode suits your setup; confirm idle-unload behavior for the exact version and configuration before relying on it.
- Consider LocalAI if choosing among its supported backends behind one client-facing API matters more than a documented built-in idle policy.
- Consider vLLM if its serving interfaces and deployment requirements fit; do not treat LoRA adapter unloading as evidence that the base model unloads when idle.
For any option, check how many requests may run at once and how much memory their contexts require. Ollama’s documentation notes that concurrent loading and processing depend on available system memory or VRAM; requests may queue when memory is insufficient, and idle models may be unloaded to make room. Parallel requests also increase memory requirements with context length. These constraints can affect the practical value of a chosen keep-alive period.
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What performance evidence is available?
The cited official documentation describes controls and lifecycle behavior, but does not provide a head-to-head comparison of latency, throughput, memory use, or wake-up time for these servers. There is no evidence here for a universal speed or memory-saving winner. If cold starts or peak memory matter to your workload, compare the specific model, context, concurrency, hardware, and settings you intend to run rather than assuming one server will be faster.
Quick Recap
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