Production Inference: From Script to System
Loading a model is easy. Keeping it running with high throughput and low latency under load is the real challenge.
The Inference Engine Ecosystem
Choosing an inference engine is a decision about your target hardware and concurrency needs.
vLLM: The Concurrency King
Uses **PagedAttention** to efficiently manage KV cache memory. Ideal for multi-user scenarios.
- High throughput
- Dynamic batching
- NVIDIA & AMD support
Ollama: The Developer's Choice
Bundles the model, runner, and configuration into a single CLI tool. Perfect for rapid prototyping.
- Zero-config setup
- MacOS (Apple Silicon) optimized
- Simple REST API
Dockerizing for the Enterprise
To bridge the intermediate gap, you must stop running models in bare-metal scripts. Use Docker to ensure environment parity across your dev, stg, and prod environments.
# Simplified vLLM Dockerfile
FROM vllm/vllm-openai:latest
ENV MODEL_NAME="mistralai/Mistral-7B-v0.1"
ENV QUANTIZATION="awq"
EXPOSE 8000
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
CMD ["--model", "\$MODEL_NAME", "--quantization", "\$QUANTIZATION"]
Monitoring and Observability
A production system is blind without metrics. Key KPIs for LLM inference include:
- TTFT (Time To First Token): Crucial for perceived user experience.
- TPS (Tokens Per Second): The overall speed of the generation.
- VRAM Utilization: Monitoring for fragmentation and OOM (Out Of Memory) risks.
Series Complete.
You now have the technical foundation to deploy private, professional-grade AI systems.
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