OpenJev CPU runtime — baseline Space spec
The recommended zero-GPU Hugging Face Space configuration for broadfield/openjev-cpu-run (the one-click Duplicate target).
| setting | value | why |
|---|---|---|
| SDK | Docker | we need llama.cpp binaries + a multi-process entrypoint (bash) |
| Hardware | CPU basic (free) | zero GPU, by design (note: Docker Spaces on cpu-basic require a PRO subscription) |
| Persistent storage | 30 GB (≈ $0.50/mo) | /data holds the 16.2 GB GGUF + tokenizer |
| Auto-sleep | any (e.g. 15 min) | wake-up reloads model from /data → ~1–2 min, not 20+ |
| Visibility | public (default) | the API is meant to be called; flip private + OPENJEV_TOKEN to lock it |
| Secrets | none required | optional OPENJEV_TOKEN via Space settings → will appear as an env var |
Approximate resource/memory math (Q4_K_M)
| item | size |
|---|---|
OpenJev-Q4_K_M.gguf on disk |
16.2 GB |
| RAM to hold the model weights | ~16.2 GB |
| KV cache (llama.cpp fork, 8-bit, 8192 ctx, 1 stream) | ~1–2 GB |
| openjev-server + transformers tokenizer | ~1–2 GB |
| peak RSS | ~18–20 GB (fits the 16 vCPU / 32 GB free tier) |
If the free tier's memory turns out tight (it runs 32 GB RAM / 16 vCPU per
current info), drop to LLAMA_CTX=4096 or use quant OpenJev-Q5_K_M.gguf.
Build-time budget
The image does, in order:
aptbuild tools — ~1 min- clone + checkout llama.cpp
b4630— ~1 min - CMake CPU build of llama-server — ~15–35 min on the build machine
pip install openjev-server(transformers etc.) — ~2–4 min- final image copy — ~1 min
First boot then downloads the GGUF (~16.2 GB) into persistent /data —
that's the long pole (20–50 min depending on HF Hub egress). Persisted
afterward, so subsequent boots are 1–2 min.
Why entrypoint waits up to 15 min for llama-server
A cold boot loads 16.2 GB off disk into RAM and runs llama.cpp's warmup; on a
shared CPU that's minutes. The entrypoint polls /v1/models until ready, then
starts openjev-server, so HF's health check never sees a crash while loading.