AI Computing - Meta's MTIA

Busy week in the world of data and AI. Today, Meta announced the company's new generation of Meta AI chips called MTIA - tuned for training and inference of data workloads for ranking and recommendation.

https://ai.meta.com/blog/next-generation-meta-training-inference-accelerator-AI-MTIA/

A few observations:

• Continued separation of training, inference and specialization of AI infrastructure and models tuned for different tasks. We saw the rise of MLOps decoupling training and deployment, and we will further see decoupling to ensure every key element of AI pipeline can be optimized.
• Generalization is good but it also can be more costly, evidenced by LLMs. Chips tuned for training and inference of Meta's data workload are complementary to GPUs which can be used for other tasks. Providing the right infrastructure given a type of data workload, data usage pattern, and use case makes a lot of sense. This can make things more efficient and allow modularity.
• Building the software stack and ecosystem to enable more effective consumption of AI infrastructure. Meta is the creator of Pytorch which is data scientists-facing, and they now optimized Triton, which is a language for AI backend, further down the stack.

Exciting times on all fronts - AI hardware, software, and apps.