Blog¶

May 13, 2026
in llm-d, Deep Dives
10 min read

From Simulation to Production: How an AI-Native Pipeline Discovered a Better Admission Controller for llm-d

A case study in closing the AI-native loop: observe, reason, change, validate, deploy.

Introduction

An AI-native system is one that continuously and autonomously closes the loop from observation to action to deployment, with AI as the primary agent driving this process. Rather than humans manually directing each improvement, humans establish objectives and boundaries while the system autonomously executes the cycle, at machine speed.

April 21, 2026
in Vision
13 min read

AI Native Systems: Autonomous Evolution at Machine Speed

Tamar Eilam, Fabio Oliveira, Michael Factor

1. Introduction: The Bottleneck in System Evolution and AI Native Systems

Modern software systems, especially those that serve AI workloads, are extraordinarily complex and must evolve continuously under pressure from new models, new hardware, changing usage patterns, and shifting business objectives. These pressures drive constant change, both in configuration and in code. Yet, even with increasingly powerful AI tools, improvement of such systems remains fundamentally human-driven. Engineers inspect logs and metrics, diagnose problems, open tickets, draft and review pull requests, extend tests, and orchestrate deployments through fragmented workflows. AI assists at each step, but progress is mediated by people, one decision at a time.

April 9, 2026
in BLIS, llm-d
1 min read

The Physics of High-Fidelity Distributed Inference Platform Simulation

Production LLM inference platforms are distributed systems where routing policies, admission control, autoscaling, and engine-level scheduling all interact to determine latencies and throughput. How do you explore how different policies and configurations affect these KPIs before deploying to production? Testing a new routing policy or autoscaling threshold on live traffic risks cascading bugs across the fleet, while building separate test environments burns GPU-hours and still cannot predict interactions between cluster-level policies and engine-level batch dynamics.

March 5, 2026
in BLIS, llm-d
1 min read

Why Simulate Before You Scale

Deploying large language models in production is one of the most expensive infrastructure decisions an organization can make. A single high-end GPU costs upwards of $30,000, and a production cluster can run into millions per year. Yet most teams make their first scaling decisions based on rough estimates, vendor benchmarks, or — worst of all — trial and error on live hardware.

What if you could test your deployment plan before spending a dollar on GPUs?