Serving an AI model at scale requires specialized infrastructure that handles the unique demands of model inference — GPU memory management, batching, and streaming.

Serving Architectures

Synchronous API: Request → Inference → Response. Best for real-time applications (chatbots, search). Requires low latency.

Asynchronous/Batch: Jobs queued and processed in bulk. Best for non-real-time workloads (document processing, report generation). Maximizes GPU utilization.

Streaming: Tokens streamed as they're generated. Essential for LLM applications where time-to-first-token matters more than total latency.

Key Serving Frameworks

vLLM: The de facto standard for LLM serving

• PagedAttention for efficient KV-cache management
• Continuous batching for maximum throughput
• Supports LoRA adapters, quantized models, and speculative decoding
• 2-4× faster than naive serving implementations

Text Generation Inference (TGI): Hugging Face's production server

• Docker-based deployment
• Built-in support for most Hugging Face models
• Flash Attention and quantization support
• Good default for getting started quickly

Triton Inference Server: NVIDIA's multi-framework server

• Supports PyTorch, TensorFlow, ONNX, and custom backends
• Advanced scheduling and batching
• Model ensembles (chain multiple models)
• Best for multi-model deployments

Ollama: For local and edge deployment

• Simple CLI-based model management
• Runs GGUF-quantized models efficiently
• Great for development and testing
• Not designed for high-throughput production

Batching Strategies

• Static batching: Wait for N requests, process together. Simple but adds latency.
• Dynamic batching: Process requests as they arrive with a short timeout window.
• Continuous batching: vLLM's approach — new requests join the batch as slots free up during generation.

Continuous batching can increase throughput by 2-5× compared to static batching.

GPU Memory Management

LLM inference is memory-bound. Key techniques:

• KV-cache optimization: PagedAttention allocates cache memory on-demand, reducing waste by 60-80%
• Model parallelism: Split large models across multiple GPUs (tensor parallel or pipeline parallel)
• Quantization: Reduce model precision from FP16 to INT8/INT4 for 2-4× memory reduction