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twinny

llama.cpp

llama.cpp is the inference engine behind most local servers. Running its own llama-server gives you full control over the model file, context size, GPU offload and threads, with the least overhead. You manage the GGUF files yourself.

Start a server

Download a GGUF (for example from Hugging Face) and start:

Terminal window
llama-server -m qwen2.5-coder-7b-instruct-q4_k_m.gguf \
  --port 8080 -c 8192 -ngl 99
  • -c sets the context size; -ngl 99 offloads every layer to the GPU.
  • Add --embedding if this server should also answer embedding requests.
  • -hf user/repo can download a model straight from Hugging Face in recent builds.

One llama-server serves one model. Run a second one on another port for a different completion model:

Terminal window
llama-server -m qwen2.5-coder-1.5b-q8_0.gguf --port 8081 -c 4096 -ngl 99

In twinny

Add provider → llama.cpp. Defaults:

JobPathNotes
Chat/v1OpenAI-compatible chat
Autocomplete/completionllama.cpp’s native route, so the FIM prompt is rendered by twinny
Embeddings/embeddingServer must run with --embedding

The model name is informational for llama.cpp, since the server has exactly one model loaded; twinny still uses it to pick the FIM template when set to Automatic, so name it after the family (qwen2.5-coder-1.5b) or choose the template by hand.

Change the port on the provider if you used something other than 8080. Because the generic OpenAI-compatible server preset also defaults to 8080, first-run discovery credits whichever answers to llama.cpp first.

Tips

  • Quantisation is in the file name: q4_k_m is the usual balance; q8_0 is near-lossless and twice the size.
  • --flash-attn and -ctk q8_0 -ctv q8_0 reduce memory for large contexts on supported GPUs.
  • The server prints each request’s timings to its console, which is useful when tuning completion latency.
  • Docker: ghcr.io/ggml-org/llama.cpp:server (or :server-cuda) with -v /path/to/models:/models -p 8080:8080 and the same flags.