asiai-inference-server 0.14.0

Fleet manager for local LLM inference engines on Apple Silicon

asiai-inference-server

Fleet manager for local LLM inference engines on Apple Silicon.

Status: v0.3.0 — released on PyPI. Lifecycle, memory reclaim, presets, upgrades, fleet push and health probing are functional. See the roadmap below for what each version shipped.

asiai-inference-server is the control plane companion to asiai (the observability/benchmark CLI). Where asiai observes what's running on your Mac, this project manages it: install, start, stop, unload, and orchestrate inference engines (llama.cpp, Ollama, LM Studio, oMLX, TurboQuant, mlx-lm, vMLX, Rapid-MLX, MTPLX, …) across one or several Apple Silicon machines.

It also fixes the long-standing macOS pain point: engine memory that never gets freed because of the unified-memory compressor. Killing a process doesn't release the VRAM. This tool combines per-engine unload APIs, full LaunchDaemon restart, and sudo purge to reclaim memory deterministically — and reports the actual delta measured, not a marketing promise.

Why

After a year of running multi-engine LLM inference on Apple Silicon (MacBook M1 Max, Mac Mini M4 Pro, MacBook M5 Max), the operational gap became obvious:

• Install/uninstall an engine should not require chasing brews, plists and firewall rules across READMEs.
• Switching profiles ("coding agent on Qwen-Coder 32B" → "70B chat on TurboQuant") should be a single command, not five.
• Memory unload should actually free the VRAM, not let the macOS compressor sit on it.
• A fleet of Macs should be a single dashboard, not three SSH sessions.
• AI agents (Claude Code, Cursor, Windsurf) should be able to manage the fleet via MCP, not just observe it.

asiai-inference-server ships these one at a time, building on the asiai observability stack.

Roadmap

Version	Scope	Status
v0.1	Install/uninstall/start/stop/restart + unload + purge memory, pf firewall anchor, sudoers bootstrap	shipped
v0.2	aisctl upgrade (brew, whitelisted), asiai versions provider, fleet Phase 2 (loopback serve + orchestrator fleet push)	shipped
v0.3	status --deep (generation probe, catches GPU-OOM zombies), install records + reinstall, single preset location, disable/enable (cold standby)	shipped
v0.4	asiai-priv privileged helper — a root-owned, content-validating helper replaces the wildcard sudoers: generate-don't-validate plists, a closed action allowlist, forced non-root run-as, and an append-only audit log; NOPASSWD is reduced to the helper alone	shipped
v0.5	SMAppService app bundle + asiai-launch (named entry with custom icon in Background Items)	in review
later	Profile switching (apply/rollback), fleet inventory, web cockpit, MCP write tools	planned

Architecture (high level)

• CLI: aisctl <command> (standalone) or asiai engine <command> (auto-injected sub-CLI when asiai-inference-server is installed alongside asiai).
• Python stdlib only for the core (consistent with asiai). The package depends on asiai>=1.15.0 (auth.loopback + the shared fleet.command_spec). Optional extras: mcp (for future write tools).
• macOS Apple Silicon only. We rely on launchctl, vm_stat, sudo purge, pfctl, iogpu.wired_limit_mb.
• HTTP agent for fleet operations (loopback serve + fleet push orchestrator, shipped v0.2).
• TOML for human-edited files (engine manifests, profiles, fleet inventory). JSON for runtime state.
• Engine API keys stay out of plists and ps: manifests declare [binary].api_key_file (path to an operator-created, mode-600 key file the engine reads at startup — e.g. llama-server --api-key-file) and [network].api_key_file (same file, used by the health/generation probes to authenticate). Only paths appear in manifests, generated plists, and process arguments — never key values.

License

Apache-2.0 © 2026 Jean-Marc Nahlovsky