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Ollama can serve APIs beyond single-user local use. In production, model fit, context length, parallel requests, loaded-model count and queue limits become critical and interact with one another.
According to Ollama's FAQ, increasing `OLLAMA_NUM_PARALLEL` scales effective context allocation with parallel requests and increases RAM/VRAM demand. `OLLAMA_MAX_LOADED_MODELS` controls concurrent loaded models and `OLLAMA_MAX_QUEUE` controls queue depth.
When a request arrives, Ollama loads the model or reuses a resident model. If memory is insufficient, other models may be unloaded or the request queued.
Raising settings does not automatically improve performance; exceeding VRAM creates queueing or unload/reload cost.
Current Ollama context documentation explicitly states longer context requires more memory. Agent and coding workloads need corresponding VRAM headroom.
Test through localhost/private networking rather than public exposure.
Turkey (Turkish)
Germany (German)
Worldwide (English)
ollama psollama listcurl -s http://127.0.0.1:11434/api/tagsnvidia-smijournalctl -u ollama --since '-15 min' --no-pagerProtect the local Ollama API with reverse proxy, VPN or private networking for TLS/auth, and manage model upgrades and volume backups separately.
It depends on `OLLAMA_NUM_PARALLEL` and available memory; verify defaults and runtime behavior on your version/hardware.
The FAQ states the server can return a 503 overloaded response when queue capacity is exceeded.
Yes. It supports `/api/embed` and embedding workflows.
Share model, context, concurrent users/requests and keep-alive expectations; we can size VRAM/RAM and queue settings.