LLM Inference Considerations

I developed this technical workshop to help engineering teams understand the fundamentals of large language model inference—from how text generation actually works under the hood to the advanced techniques that make production deployments feasible. As LLMs have become central to modern applications, teams increasingly need to grasp not just how to call an API, but how to optimize performance, control costs, and deploy models effectively.

The workshop begins with essential background on the transformer architecture, focusing on the self-attention mechanism that enables contextual understanding of language. I walk through how decoders work in practice, demonstrating the auto-regressive token generation process that powers today's LLMs like GPT and Llama. This foundation helps teams understand why certain optimizations matter and how different architectural choices impact performance.

A key section covers text generation methods and inference parameters. I explain the various decoding strategies—from greedy search and beam search to more sophisticated sampling techniques like top-k and top-p (nucleus sampling). The workshop dives deep into temperature scaling and how these parameters affect creativity, determinism, and output quality. This practical knowledge enables teams to fine-tune their applications for specific use cases, whether they need consistent outputs or more creative generation.

The core focus shifts to efficient inference techniques that address real-world deployment challenges. I cover quantization methods for reducing memory requirements, explaining how models can run in 8-bit or 4-bit precision with minimal quality degradation—often the difference between needing expensive multi-GPU setups or running on commodity hardware. Flash attention gets significant coverage as a breakthrough that makes long-context applications feasible by solving the quadratic memory scaling problem of traditional attention.

I also explore KV-caching, which dramatically speeds up sequential generation by avoiding redundant computations, and dynamic batching techniques that maximize GPU utilization in production serving scenarios. For teams deploying larger models, I explain tensor parallelism as a strategy for distributing model weights across multiple GPUs efficiently.

The workshop concludes with practical guidance on serving frameworks like Text Generation Inference (TGI), which implement these optimizations transparently. I emphasize that while many of these techniques operate behind the scenes, understanding them helps teams make informed decisions about model selection, hardware requirements, and deployment strategies.

Throughout, I balance technical depth with practical applicability—teams leave understanding both the "why" behind modern inference optimizations and the "how" of implementing them in their own systems. The goal is to demystify LLM inference and provide actionable knowledge for building efficient, cost-effective LLM-powered applications.