llama.cpp Wake-on-Demand Proxy

Two llama-server.exe router instances behind small Python proxies, exposed over a cloudflared tunnel under a single hostname. proxy.py reverse-proxies /embedding/* to the embed stack. Each proxy loads its model on first request and unloads after 10 minutes of inactivity — the router process stays up so the tunnel never breaks.

ai.example.com/chat/*       → :8001 proxy.py     → :8002 router (chat)
ai.example.com/embedding/*  → :8001 proxy.py     → :8003 embed_proxy.py → :8004 router (embeddings)

Bare ai.example.com/v1/... at the root also still hits the chat router (backwards compat); the /chat prefix is the preferred public alias. Both stacks run side-by-side as independent processes; both models can be loaded concurrently.

Files

File	Role
proxy.py	Chat proxy + router supervisor. Generates models-preset.ini at startup. Also reverse-proxies /embedding/* to the embed stack.
embed_proxy.py	Embedding/re-ranking proxy + router supervisor. Generates embed-preset.ini.
watchdog.ps1	Restart-on-crash supervisor for proxy.py.
watchdog-embed.ps1	Restart-on-crash supervisor for embed_proxy.py.
restart-watchdog.ps1	Graceful midnight restart for chat watchdog (WM_CLOSE → cascade shutdown → relaunch).
restart-watchdog-embed.ps1	Same for embed watchdog, staggered 1 min after chat.
create-scheduler-tasks.ps1	Creates daily Task Scheduler entries (run once as Administrator).
log_paths.py	Shared log path resolution and formatting utilities.
log_paths.ps1	PowerShell helper for inspecting log paths.
chat_template.jinja	Custom Jinja chat template passed to llama-server.
models-preset.ini	Auto-generated (don't edit). Every MODELS × CTX_CHOICES combo as its own preset.
embed-preset.ini	Auto-generated (don't edit). Single preset for Qwen3-Embedding-4B.
models/	GGUF weights. models/_aux/ holds mmproj projectors.
llama.cpp_latest/llama-server.exe	The router binary.

Prerequisites

1. Python 3.x — create and activate a virtualenv:

   python -m venv .venv
   .venv\Scripts\activate

2. Install dependencies — only aiohttp is required:

   pip install aiohttp

3. Download llama-server.exe — get a Windows build from llama.cpp releases (b9209+ for MTP support) and place it in llama.cpp_latest/.
4. Download GGUF models — place your model files in the models/ directory.
5. Set your API key — export the environment variable (overrides the hardcoded fallback in the proxy code):

   $env:LLAMA_API_KEY = "your-secret-key"

Run it

Two terminals for the proxies (service them however you like — they're independent):

H:\llama.cpp\watchdog.ps1                  # chat stack on :8001
H:\llama.cpp\watchdog-embed.ps1            # embed stack on :8003

Daily restart — create Task Scheduler entries so watchdogs restart cleanly at midnight:

H:\llama.cpp\create-scheduler-tasks.ps1    # run once as Administrator

Public tunnel — cloudflared is set up separately (outside this repo). Install cloudflared.exe, configure your tunnel in ~/.cloudflared/config.yml, then run it however you prefer.

Headless chat variant (skip the interactive picker — sets the fallback model when a client request omits the model field; all combos are still routable):

H:\llama.cpp\watchdog.ps1 --model "Qwen3.6-35B-A3B Q3" --ctx-size 32768

Endpoints

• Chat: https://ai.example.com/chat/v1/chat/completions — model field selects preset (e.g. qwen3.6-35b-q3-32k).
• Embeddings: https://ai.example.com/embedding/v1/embeddings — model qwen3-embedding-4b-8k.
• Re-ranking: https://ai.example.com/embedding/v1/rerank — same embed router.
• Both require Authorization: Bearer <key>. Set via $env:LLAMA_API_KEY (overrides the hardcoded fallback in the proxy code).

/health on either port reports proxy + router state and which model is currently loaded.

How the load/unload flow works

1. Watchdog starts the proxy. Proxy spawns the router with --no-models-autoload and --models-max 1. GPU is idle, router is healthy.
2. First chat/embedding request arrives. Proxy POSTs /models/load to the router, polls /v1/models until status.value == "loaded", then forwards.
3. Idle watchdog (inside each proxy) checks every 30s. After 10 minutes of no activity it POSTs /models/unload. VRAM frees; router stays running; tunnel socket stays alive.
4. Next request reloads on demand.

Cold-load latency: ~15–20s for the 35B chat model on a 3090 Ti; ~2–3s for the 4B embedder.

Adding a model

Chat side — append a ModelChoice(...) to MODELS in proxy.py. Every entry is automatically crossed with CTX_CHOICES (32k, 64k, 96k, 128k) to produce one preset per (model × ctx) pair. No INI editing.

Each preset uses KV cache quantization (cache-type-k = q4_0, cache-type-v = q4_0), flash attention, and a custom Jinja chat template (chat_template.jinja with preserve_thinking kwarg).

MTP (speculative decoding) — set spec_mtp=True on a ModelChoice to enable built-in MTP speculative decoding. This emits spec-type=draft-mtp, spec-draft-n-max=2, spec-draft-p-min=0.0 in the preset. Requires the b9209+ router binary (PR ggml-org/llama.cpp#22673). The 27B model has an MTP variant (qwen3.6-27b-q4-mtp) that uses models/Qwen3.6-27B-UD-Q4_K_XL.mtp.gguf.

Embed side — embed_proxy.py is hardcoded to a single model (MODEL_FILE, MODEL_ID, CTX_SIZE constants at the top). Change those if you swap the embedder.

Logs

• logs/<week>/proxy-<date>.log — chat proxy events
• logs/<week>/embed-proxy-<date>.log — embed proxy events
• logs/<week>/llama-server-<date>.log — chat router output
• logs/<week>/embed-server-<date>.log — embed router output
• logs/<week>/chat-<date>.log and chat-<date>.raw.jsonl — per-request chat traces (proxy.py only)
• logs/<week>/watchdog-restart-<date>.log — daily restart logs
• All logs are bucketed by ISO week folder.