Llm-Inference

NVIDIA Dynamo: A Practical Guide to the Open-Source Inference Serving Framework

Dynamo disaggregates prefill and decode, distributes KV cache across nodes, and routes traffic intelligently. Here’s what it is, how it works, and when to reach for it in production.

Diagram of a prefix tree being shared across concurrent LLM requests.

SGLang in Production: How Structured Generation Makes LLM Serving Actually Fast

SGLang is a serving stack co-designed around how real LLM programs actually look — structured prompts, tool calls, and multi-turn agents. We dig into RadixAttention, the DSL, and what changes when you deploy it next to vLLM.

Abstract visualization of a transformer model being compressed into an optimized form.

NVIDIA Model Optimizer: The Compiler-Style Playbook for Production LLM Inference

A deep dive into NVIDIA’s Model Optimizer toolkit and how its compilation-style approach reshapes the production LLM inference stack, from quantization recipes to KV-cache strategies.

A diagram showing the stages of an LLM inference request moving from the API edge through the scheduler, KV cache, and GPU.

The LLM Inference Roadmap I Wish More Engineers Followed

Most teams treat LLM inference like a REST call. This roadmap walks through the layers that actually decide cost and latency in production — from continuous batching to KV cache layout to speculative decoding.

Optimizing Vector Database Retrieval for Low Latency LLM Inference in Distributed Edge Environments

Table of Contents Introduction Background Edge Computing & LLM Inference Constraints Vector Databases: A Quick Primer Latency Bottlenecks in Distributed Edge Retrieval Architectural Patterns for Low‑Latency Retrieval Indexing Strategies Tailored for Edge Data Partitioning and Replication Optimizing Network Transfer Hardware Acceleration on the Edge Practical Code Walkthrough Monitoring, Observability, and Adaptive Tuning Real‑World Use Cases Future Directions Conclusion Resources Introduction Large language models (LLMs) have moved from data‑center‑only research prototypes to production‑grade services that power chatbots, code assistants, and generative applications. As these models become more capable, the demand for low‑latency inference—especially in edge environments such as smartphones, IoT gateways, autonomous drones, and retail kiosks—has skyrocketed. ...