Running an AI model in production is a different problem from training it: teams need it to answer fast, run cheaply, and work on whatever hardware they can get their hands on — Nvidia GPUs, AMD GPUs, or increasingly other specialized chips. Modular is a platform built to solve exactly that "last mile" problem, letting a model run efficiently across radically different hardware without an engineering team having to hand-optimize it for each one. It's built around Mojo, a Python-interoperable programming language for systems-level and GPU performance, and MAX, a framework for hardware-agnostic inference across NVIDIA, AMD, TPU, Trainium, Intel, ARM, and Apple Silicon. It exposes OpenAI-compatible APIs, targets sub-500ms time-to-first-token, and supports three deployment modes: managed cloud, private cloud, or fully self-hosted. Benchmarks show 171% of vLLM's throughput on AMD MI355x hardware. The core is Apache 2.0 licensed, with MAX under the Modular Community License; the project has 29,000+ GitHub stars and active support for emerging model families like DeepSeek V3/R1 and Kimi K2.7.
| # | Tool | Best for | Price |
|---|---|---|---|
| 1 | Développeurs et organisations qui déploient des LLM en production, du débutant à l'expert | — | |
| 2 | Développeurs | — | |
| 3 | Développeurs | — | |
| 4 | Développeurs | — |
Running a large language model efficiently at scale is a genuinely hard engineering problem — vLLM is the open-source engine that many AI companies quietly rely on to serve LLMs fast and cheaply, without you having to solve GPU memory management yourself.
