Tag: AI/ML Techniques

In this article, you will learn how prompt caching and fine-tuning differ as strategies for reducing cost and latency in agentic AI systems, and how…

But cutting your runtime token burn is just the first problem.

With the vocabulary and the failure modes in place, here’s the build.

Day 100 in production isn’t really about chunking strategies anymore.

This chapter is divided into eight parts; they are: • Metrics for LLM Inference • Measuring a Single Request • Warmup and Synchronization • Measuring GPU Work with CUDA Events • Measuring Memory Usage • Measuring Concurrent Requests • Multiple GPUs and Multiple Machines • Cost per Token The most common inference metrics are: • …

Read more “Measuring Performance of Transformer Inference”

In this article, you will learn how static, dynamic, and continuous batching work in LLM inference, and why the differences between them matter at production…

This chapter is divided into four parts; they are: • Autoregressive Generation • Prefill and Decode • A Simple KV Cache • Memory Usage of the KV Cache A decoder-only transformer model predicts the next token from the tokens that came before it.

This chapter is divided into nine parts; they are: • Reading Logits from a Model • Greedy Decoding • Temperature Sampling • Top-$k$ Sampling • Nucleus Sampling • Repetition Penalties • Beam Search • Stop Conditions • Structured Output Constraints The model returns a vector of logits for every position in the input sequence.

In this article, you will learn the seven architectural components that separate a production-grade agentic AI system from a demo script, and how each one…

In this article, you will learn how Ollama, LM Studio, and llama.cpp differ across the dimensions that matter most to practitioners, and how to choose…