Serving LLMs requires specialized infrastructure. vLLM batches requests and pages KV cache. TGI (Text Generation Inference) optimizes throughput. Ollama runs models locally. Each framework optimizes different objectives: latency vs. throughput.
vLLM: High-Throughput Serving
vLLM batches requests and uses paged attention (like virtual memory for KV cache) to support high throughput.
# Start vLLM server
python -m vllm.entrypoints.openai_api_server \ --model meta-llama/Llama-2-7b \ --tensor-parallel-size 2 \ --gpu-memory-utilization 0.9
TGI: Text Generation Inference
TGI optimizes latency with continuous batching and speculative decoding.
# Start TGI server
docker run --gpus all -p 8080:80 -v $MODEL_DIR:/data ghcr.io/huggingface/text-generation-inference:latest
Ollama: On-Device Inference
Run models locally without cloud infrastructure.
# Install and run
ollama pull llama2 ollama run llama2 "What is 2+2?"
Ollama is ideal for privacy-sensitive applications, offline use, and development.
Conclusion
Model serving frameworks enable production LLM deployment. vLLM and TGI optimize cloud serving; Ollama enables local inference. Choosing the right framework depends on latency/throughput requirements and deployment constraints. Next: monitoring and observability for production LLM systems.