Meta Platforms Inc. today revealed that it has designed foour custom chips to power its internal artificial intelligence workloads.

The company last provided an update about its processor development efforts in 2024. In April of that year, it revealed a custom AI accelerator with an energy footprint of 90 watts. The most advanced of the four accelerators that Meta debuted today has a thermal design point of 1,700 watts.

The custom chip that the company revealed in April 2024, the MTIA 200, was designed solely to run ranking and recommendation models. Those are neural networks that Meta uses to determine what posts and ads to display in users’ feeds.

The first new chip that it unveiled today, the MTIA 300, is focused on the same use cases. It can provide 1.2 petaflops of performance when processing data in the MX8 format and features 216 gigabytes of HBM memory.

“MTIA 300 comprises one compute chiplet, two network chiplets, and several HBM stacks,” a group of Meta engineers wrote in a blog post today. “Each compute chiplet comprises a grid of processing elements (PEs), with some redundant PEs to improve yield.”

The MTIA 300 is the only one of the four newly revealed chips that Meta has already deployed in production. The three other processors support a broader range of use cases. Besides ranking and recommendation workloads, they can also run generative AI software such as large language models.

The most advanced chip in the lineup, the MTIA 500, can provide 10 petaflops of performance when processing MX8 data. It also supports a more efficient data format called MX4. The latter technology reduces the number of bytes that AI models must analyze to answer prompts, which speeds up processing.

The MTIA 500 carries out calculations using four logic chiplets. The modules are surrounded by multiple stacks of HBM memory that can together store up to 516 gigabytes of data, or twice as much as the MTIA 300. Rounding out the processor’s list of components is a so-called SoC chiplet that is responsible for moving information to and from the host server.

The MTIA 500 is expected to enter production in 2027 alongside a similar but less advanced chip called the MTIA 450. Both processors are optimized for generative AI inference workloads. They include circuits that are designed to accelerate specific, hardware-intensive elements of the inference workflow such as FlashAttention. That’s a popular implementation of the attention mechanism with which LLMs analyze input data.

“At the system level, MTIA 400, 450, and 500 all utilize the same chassis, rack, and network infrastructure,” the Meta engineers wrote. “Therefore, each new chip generation can be dropped into the same physical footprint, accelerating the transition from silicon to production deployment. Our modular, reusable designs also minimize the resources needed to develop and deploy multiple chip generations.”

Meta uses custom compilers to optimize AI models for its MTIA chips. Another custom software module, the Hoot Collective Communications Library, manages the flow of data between the processors. It carries out certain calculations using transistors that are located near memory cells, which reduces data travel times and thereby boosts performance.

The chips’ debut comes less than a month after Meta agreed to buy billions of dollars worth of processors from Nvidia Corp. and Advanced Micro Devices Inc. Around the same time, sources told The Information that the Facebook parent also plans to adopt Google LLC’s TPU accelerators. It will reportedly use the chips to run LLMs.

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