docs/basic_usage/ollama_api.md
SGLang provides Ollama API compatibility, allowing you to use the Ollama CLI and Python library with SGLang as the inference backend.
# Install the Ollama Python library (for Python client usage)
pip install ollama
Note: You don't need the Ollama server installed - SGLang acts as the backend. You only need the
ollamaCLI or Python library as the client.
| Endpoint | Method | Description |
|---|---|---|
/ | GET, HEAD | Health check for Ollama CLI |
/api/tags | GET | List available models |
/api/chat | POST | Chat completions (streaming & non-streaming) |
/api/generate | POST | Text generation (streaming & non-streaming) |
/api/show | POST | Model information |
python -m sglang.launch_server \
--model Qwen/Qwen2.5-1.5B-Instruct \
--port 30001 \
--host 0.0.0.0
Note: The model name used with
ollama runmust match exactly what you passed to--model.
# List available models
OLLAMA_HOST=http://localhost:30001 ollama list
# Interactive chat
OLLAMA_HOST=http://localhost:30001 ollama run "Qwen/Qwen2.5-1.5B-Instruct"
If connecting to a remote server behind a firewall:
# SSH tunnel
ssh -L 30001:localhost:30001 user@gpu-server -N &
# Then use Ollama CLI as above
OLLAMA_HOST=http://localhost:30001 ollama list
import ollama
client = ollama.Client(host='http://localhost:30001')
# Non-streaming
response = client.chat(
model='Qwen/Qwen2.5-1.5B-Instruct',
messages=[{'role': 'user', 'content': 'Hello!'}]
)
print(response['message']['content'])
# Streaming
stream = client.chat(
model='Qwen/Qwen2.5-1.5B-Instruct',
messages=[{'role': 'user', 'content': 'Tell me a story'}],
stream=True
)
for chunk in stream:
print(chunk['message']['content'], end='', flush=True)
For intelligent routing between local Ollama (fast) and remote SGLang (powerful) using an LLM judge, see the Smart Router documentation.
| Component | Purpose |
|---|---|
| Ollama API | Familiar CLI/API that developers already know |
| SGLang Backend | High-performance inference engine |
| Smart Router | Intelligent routing - fast local for simple tasks, powerful remote for complex tasks |