LLMOps

LLMOps (Large Language Model Operations) is the set of practices for deploying, monitoring, and maintaining LLMs in production. For operators running local AI, LLMOps covers tasks like serving models via APIs (e.g., vLLM, Ollama), managing model versions, handling inference requests, and monitoring latency and resource usage (VRAM, CPU). It extends MLOps to address LLM-specific challenges: prompt engineering, context window management, quantization for hardware constraints, and cost/performance trade-offs between local and cloud inference.

LLMOps emerged as LLMs grew too large for simple deployment. Key components include: 1) Model serving: using frameworks like vLLM (PagedAttention for efficient VRAM) or Ollama (simplified local serving). 2) Prompt management: versioning prompts, handling context windows, and ensuring consistent outputs. 3) Monitoring: tracking tokens/sec, VRAM usage, and response quality. 4) Lifecycle management: updating models (e.g., from Llama 3.1 8B to 70B) while maintaining uptime. 5) Cost optimization: choosing quantization levels (Q4 vs Q8) to fit hardware. For local operators, LLMOps often means balancing model size against available VRAM and latency requirements.

An operator runs a local chatbot using Ollama with Llama 3.1 8B Q4. They monitor VRAM usage (5 GB) and tokens/sec (40). To serve multiple users, they switch to vLLM with continuous batching, which increases throughput but requires more VRAM. They also set up a dashboard tracking inference latency and model drift (e.g., if responses become repetitive).

In practice, LLMOps involves: 1) Serving: ollama serve starts a REST API. 2) Monitoring: nvidia-smi checks VRAM; custom scripts log tokens/sec. 3) Updating: ollama pull llama3.1:70b downloads a larger model, then the operator adjusts quantization (e.g., Q4_K_M) to fit VRAM. 4) Scaling: if VRAM is insufficient, offload layers to system RAM via --num-gpu-layers in llama.cpp, trading speed for capacity.