Running a Local Reranker with llama.cpp
- 3 minutes read - 620 wordsOver the past few days, I’ve been using reranker models through cloud APIs as part of my RAG experiments. After successfully running several LLMs locally with llama.cpp, I started wondering:
Can I also run a reranker model locally?
The answer is yes.
It turns out that llama-server can host embedding models, reranker models, and chat models behind the same OpenAI-compatible API. This makes it easy to build a completely local RAG pipeline without relying on external services.
In this post, I’ll share my setup along with a short recording demonstrating it in action.
Models
For this experiment I downloaded three different types of models:
-
Embedding model -
Qwen3-Embedding-4B -
Reranker model -
Qwen3-Reranker-4B -
Vision/Chat model -
Qwen3-VL-8B-Instruct
The models were downloaded directly from Hugging Face.
hf download hf://Qwen/Qwen3-Embedding-4B-GGUF/Qwen3-Embedding-4B-Q4_K_M.gguf
hf download hf://Qwen/Qwen3-VL-8B-Instruct-GGUF/Qwen3VL-8B-Instruct-Q8_0.gguf
hf download hf://Qwen/Qwen3-VL-8B-Instruct-GGUF/mmproj-Qwen3VL-8B-Instruct-F16.gguf
hf download hf://Voodisss/Qwen3-Reranker-4B-GGUF-llama_cpp/Qwen3-Reranker-4B-Q4_K_M.gguf
Configuring llama-server
I configured all three models in a single models.ini file.
Notice the important differences:
-
The embedding model enables
embedding=true. -
The reranker model enables
reranking=truewithpooling=rank. -
The vision model specifies an
mmprojfile.
cat <<EOF > models.ini [*] # Global defaults — applied to every model unless overridden n-gpu-layers = all batch-size = 2048 ubatch-size = 2048 # Pre-load the embedding model on server start load-on-startup = Qwen3-Embedding-4B-Q4_K_M # models--Qwen--Qwen3-Embedding-4B-GGUF [Qwen3-Embedding-4B-Q4_K_M] model = /home/jackl/.cache/huggingface/hub/models--Qwen--Qwen3-Embedding-4B-GGUF/snapshots/f4602530db1d980e16da9d7d3a70294cf5c190be/Qwen3-Embedding-4B-Q4_K_M.gguf embedding = true pooling = mean ctx-size = 32768 [Qwen3-Reranker-4B-Q4_K_M] model = /home/jackl/.cache/huggingface/hub/models--Voodisss--Qwen3-Reranker-4B-GGUF-llama_cpp/snapshots/1b452c803342e73ac3644551b727dfd51a09fd5b/Qwen3-Reranker-4B-Q4_K_M.gguf reranking = true pooling = rank embedding = true ctx-size = 32768 [Qwen3VL-8B-Instruct-Q8_0] model = /home/jackl/.cache/huggingface/hub/models--Qwen--Qwen3-VL-8B-Instruct-GGUF/snapshots/f982a07559d4a2f6c8744d840bf6fccab30eea96/Qwen3VL-8B-Instruct-Q8_0.gguf ctx-size = 32768 mmproj = /home/jackl/.cache/huggingface/hub/models--Qwen--Qwen3-VL-8B-Instruct-GGUF/snapshots/f982a07559d4a2f6c8744d840bf6fccab30eea96/mmproj-Qwen3VL-8B-Instruct-F16.gguf EOF
Starting the Server
Launching the server is straightforward.
llama-server \
--host 127.0.0.1 \
--port 8081 \
--metrics \
--models-max 1 \
--models-preset models.ini
Although three models are configured, --models-max 1 ensures that only one model is loaded into memory at a time. The server automatically swaps models when requests target a different model.
This is especially useful when running multiple GGUF models on a machine with limited GPU memory.
Testing the Embedding Endpoint
The embedding model exposes the familiar OpenAI-compatible /v1/embeddings endpoint.
curl http://localhost:8081/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen3-Embedding-4B-Q4_K_M",
"input": [
"The Labour Code requires 30 calendar days written notice."
]
}'
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Testing the Reranker
The reranker uses the /v1/rerank endpoint.
curl http://localhost:8081/v1/rerank \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen3-Reranker-4B-Q4_K_M",
"query": "employment termination notice period",
"documents": [
"The Labour Code requires 30 calendar days written notice.",
"Corporate tax rates for small enterprises."
]
}'
{"model":"Qwen3-Reranker-4B-Q4_K_M","object":"list","usage":{"prompt_tokens":171,"total_tokens":171},"results":[{"index":0,"relevance_score":0.6195521950721741},{"index":1,"relevance_score":1.6342601156793535e-05}]}
The reranker returns relevance scores, allowing your RAG application to reorder retrieved documents before sending the best candidates to an LLM.
Testing the Chat Model
The same server can also serve a chat model.
curl http://localhost:8081/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen3VL-8B-Instruct-Q8_0",
"messages": [
{
"role": "user",
"content": "Hello!"
}
],
"max_tokens": 128
}'
{"choices":[{"finish_reason":"stop","index":0,"message":{"role":"assistant","content":"Hello! How can I assist you today? 😊"}}],"created":1785448744,"model":"Qwen3VL-8B-Instruct-Q8_0","system_fingerprint":"b1-2f6c815","object":"chat.completion","usage":{"completion_tokens":12,"prompt_tokens":10,"total_tokens":22,"prompt_tokens_details":{"cached_tokens":0}},"id":"chatcmpl-Qn98v0krtsMpa7cqCcVpIBnvaphkNfHr","timings":{"cache_n":0,"prompt_n":10,"prompt_ms":55.836,"prompt_per_token_ms":5.5836,"prompt_per_second":179.09592377677484,"predicted_n":12,"predicted_ms":430.654,"predicted_per_token_ms":35.88783333333333,"predicted_per_second":27.864596636743187}}
Why This Is Interesting
With a single llama-server instance, it’s possible to host:
-
Embedding models
-
Reranker models
-
Chat/LLM models
-
Vision-language models
All of them are exposed through OpenAI-compatible APIs, making them easy to integrate into existing applications and RAG frameworks.
For anyone building a local RAG stack, this provides almost everything needed without depending on external APIs.
Demo
Below is a recording showing the setup in action.
Final Thoughts
This was a fun experiment that answered a question I’d been curious about after using hosted reranking APIs.
I was pleasantly surprised by how seamless the experience is with llama.cpp. Running embeddings, reranking, and chat models behind a single server significantly simplifies local AI development, and it opens the door to building fully offline RAG applications with standard OpenAI-compatible endpoints.
I’m looking forward to exploring how this setup performs in larger retrieval pipelines and experimenting with different embedding and reranking model combinations.