LLM Inference Practical Handbook: A Complete Guide from Serverless to Edge Deployment

This handbook is a code-first guide for ML engineers and backend developers, delving into the working principles of LLM inference, covering stateless and stateful inference, KV caching mechanisms, and deployment strategies from Serverless to local GPUs. It helps developers advance from surface-level API calls to a deep understanding of the inference layer, optimizing latency and costs in production environments.

Most LLM tutorials stay at the surface-level usage; this handbook fills the gap in in-depth exploration of the inference layer. It targets ML engineers, backend developers, and inference layer practitioners, providing a systematic learning path whether for advancing inference layer understanding or optimizing production environments.

The handbook adopts a progressive design:

Basic Section: Serverless Inference

• Stateless inference: Understand basic calling patterns through single-turn dialogue scripts
• Streaming output: Token-level responses to enhance user experience
• Multi-turn dialogue and history management: Maintain a messages array to achieve context awareness

Advanced Section: KV Caching and Local Deployment

• KV caching principle: Resolve computational redundancy in autoregressive decoding
• Local inference implementation: KV caching code example based on HuggingFace Transformers

Comparison Between Stateless and Stateful Inference

• Stateless: Cannot associate Paris context in three-turn dialogue, asks to clarify the city
• Stateful: Passes complete history, correctly answers Paris's weather in June

KV Caching Working Mechanism

Avoids repeated computation by storing K/V projections of past tokens; latency in long dialogues decreases significantly, and cost is only related to the number of new tokens

Experimental Environment Support

• Zero GPU: Run basic scripts using Hugging Face Serverless API
• Local GPU: CUDA12.1-compatible GPU with 14GB VRAM to run 7B models (e.g., Qwen2.5-7B)

• Serverless advantages: Zero operation and maintenance, pay-as-you-go; suitable for prototypes and low traffic
• Local deployment advantages: Controllable data privacy, no API costs; suitable for high traffic and low latency scenarios
• KV caching benefits: Reduces long dialogue costs by an order of magnitude; essential optimization for production environments

Run scripts in order to build a complete understanding: Basic inference → Streaming output → Chat history → KV caching (Stateless → Real-time UX → Stateful multi-turn → Token-level caching) Each script is accompanied by detailed comments and GIF demos to intuitively show the effect.

The handbook bridges the gap between theory and practice; it does not provide black-box code but helps build an understanding of the inference layer through experiments. It provides LLM application teams with decision-making basis from prototype to production, and serves as a high-quality textbook for ML engineering students. Its open-source nature supports the community in continuously contributing new scenarios and optimization techniques.