Introduction

InferenceXv2 (formerly InferenceMAX) builds on the foundation established by InferenceMAXv1, our open-source, continuously updated inference benchmark that has set a new standard for AI inference performance and economics. InferenceMAXv1 moved beyond static, point-in-time benchmarks by running continuous tests across hundreds of chips and popular open-source frameworks. Free dashboard available here.

Our benchmark has been widely reproduced, validated and/or supported by almost every major buyer of compute from Google Cloud to Microsoft Azure to Oracle, OpenAI, and many more.

InferenceXv2 builds on this foundation. It expands coverage to include large scale DeepSeek MoE disaggregated inference (disagg prefill, or simply “disagg”) with wide expert parallelism (wideEP) optimization to all 6 NVIDIA western GPU SKUs from the past 4 years as well as to every single AMD western GPU SKU released in the past 3 years – in total InferenceXv2 utilizes close to 1000 frontier GPUs for a full benchmark run across all SKUs.

With today’s release, InferenceXv2 is now the first suite to benchmark the Blackwell Ultra GB300 NVL72 and B300 across the whole pareto frontier curve, and it is the first third party benchmark to test disagg+wideEP multi-node FP4 and FP8 MI355X performance. In future iterations of InferenceX, we will continue to focus heavily on disaggregated serving with wide expert parallelism as that is what is deployed in production at Frontier AI Labs like OpenAI, Anthropic, xAI, Google Deepmind, DeepSeek as well as advanced API providers like TogetherAI, Baseten, and Fireworks. In this article, we will also break down the system engineering principles and economics in play around the latest Claude Code Fast mode feature.

Our benchmark is completely open-source under Apache 2.0 – this means that we are able to move at the same rapid speed at which the AI software ecosystem is advancing. If you like our work and would like to show us some support, please drop a star on our GitHub! We also provide a free data visualizer at https://inferencex.com for everyone in the ML community to explore the complete dataset themselves.

We will add DeepSeekv4 and other popular Chinese frontier models with day 0 support as over the past 6 months, we now have cleaned up a lot of tech debt and are able to move fast with stable infrastructure. We will also be adding TPUv7 Ironwood and Trainium3 to InferenceX later this year! If you want to contribute to our ...