Deployment — LLM Toolkit

Monitoring: Track latency, throughput, error rate, and GPU utilization
Autoscaling: Scale GPU instances based on queue depth or request rate
Model versioning: Keep old versions running while testing new ones
Rate limiting: Prevent abuse and manage GPU costs
Caching: Cache frequent prompts to reduce redundant computation

DEPLOYMENT PATTERNS

Run on your own machine. Use Ollama, llama.cpp, or LM Studio. Best for development and personal use.

Dedicated GPU machine serving via vLLM or TGI. Good for team access and moderate traffic.

Containerized deployment on cloud GPU instances. Auto-scaling, load balancing, monitoring.

QUANTIZATION FOR DEPLOYMENT

Halves model size with minimal quality loss. Supported natively on most modern GPUs. Good first step for deployment.

Post-training quantization to 4-bit. 4x size reduction. GPTQ and AWQ are the most common methods. Minor quality impact on well-calibrated models.

Universal quantization format supporting CPU + GPU offloading. Q4_K_M, Q5_K_M, Q6_K are popular middle-ground quantizations.

OPERATIONAL CONSIDERATIONS