MatX : LLM Inference用半導体を開発しようとしているStart up


Xの投稿に流れてきた MatX という会社。

今日は、その、MatX について、調べてみました。

MatX High throughput chips for LLMs


MatX designs hardware tailored for the world’s best AI models: We dedicate every transistor to maximizing performance for large models.

Other products put large models and small models on equal footing; MatX makes no such compromises. For the world’s largest models, we deliver 10× more computing power, enabling AI labs to make models an order of magnitude smarter and more useful.


  • We dedicate every transistor to maximizing performance for large models.


Our product
We focus on cost efficiency for high-volume pretraining and production inference for large models. This means:

とりあえず。Inference っぽい。

We’ll support training and inference. Inference first.

とはいっても、Training はサポートする。でも、Inference が最初

We optimize for performance-per-dollar first, and for latency second.
We’ll offer the best performance-per-dollar by far.


We’ll provide competitive latency, e.g. <10ms/token for 70B-class models.
Our target workloads, where we expect to achieve peak performance:
Transformer-based models with 7B+ (ideally 20B+) activated parameters, including both dense and MoE models.

7B, 20B, 70B ぐらいなのか?

For inference: at least thousands of simultaneous users.


For training: at least 1022 total training FLOPs (7B-class).

Training では、7B-class で 1022 total FLOPS って、どのぐらい凄いのかは、よくわからん。

We offer excellent scale-out performance, supporting clusters with hundreds of thousands of chips.

100,000 x n ぐらいのクラスタは頑張る!

We give you low-level control over the hardware; we know that expert users want that.

low-level control もどこかできいたことがある。

What our product enables
With our hardware:

The world’s best models will be available 3-5 years sooner.
Individual researchers can train 7B-class models from scratch every day, and 70B-class models multiple times per month.

7B-class だと、毎日数回スクラッチで学習できる。70B-class だと、月に何回が Trainigできる。。。

Any seed-stage startup can afford to train a GPT-4-class model from scratch and serve it at ChatGPT levels of traffic.

GPT-4-class でもお金なくても学習できる!


とにかく、お安く、Inference および Training できるもの作ること言うことっぽい。




  • Reiner Pope san (cofounder and CEO) : 2022.11
    • .... (2012.4 - 2022.11)
  • Mike Gunter -san (cofounder and CTO) : 2022.11
    • SGI(1996.7 - 1999.9) .... (2005.10 - 2022.11)
  • Avinash Mani -san (Chief Development Officer, Silicon) : 2024.1 -
    • Riverstone Networks (2000.6 - 2002.9), Synopsys (2002.9 - 2005.3), Axion Design Automation (2005.3 - 2006.7), Broadcom (2006.6 - 2015.1), Innovium (2015.1 - 2021.4), Amazon (22021.4 - 2023,12) .

Member は、ここにまとめました。


  • ASIC/SOC CAD Engineer
  • ASIC/SOC Micro-Architect and RTL Design Engineer
  • ASIC/SOC Silicon Design-For-Test Lead
  • ASIC/SOC Silicon Physical Design Engineer
  • ASIC/SOC Silicon Verification Engineer
  • System Hardware Lead

お賃金 : $120,000 - $400,000 + equity + benefits



The world’s best models will be available 3-5 years sooner.


の筆頭に、Reiner Pope san どうなんですかね。

ACKNOWLEDGMENTSに、Mike Gunter -san も。