Meta covers the hardware, networking and reliability work needed to train LLMs across very large GPU clusters.
Meta turned its New Albany, Ohio data center site from web traffic into Prometheus, a gigawatt-scale AI supercluster spanning multiple interconnected buildings that behaves as one machine. It runs NVL72 racks (Catalina on GB200 with 800G of scale-out per GPU, Clemente on GB300 in a single rack) on a dedicated Ethernet back-end network for GPU-to-GPU traffic. Reliability work pushed pre-training goodput above 85% and mean time between interruptions past 54K GPU hours, and the cluster is growing toward roughly 1 million GPUs.
Meta replaced separate per-surface ad prediction systems with GEM, one central recommendation foundation model trained across all of its ad surfaces. A new LLM-style architecture (Kunlun) showed LLM-like scaling with 2.5x more model complexity, while custom low-precision kernels and 5D parallelism doubled end-to-end training efficiency to 20-25% MFU at 4x the training FLOPs across thousands of GPUs. Post-training transfer (LoopFM) and elastic pre-computation (Solaris) pass GEM's gains to real-time serving models without extra latency.
Shopify groups the same real-world product sold by different merchants under one Universal Product Identifier. Embedding nearest-neighbor search finds plausible pairs across billions of listings, then three scorers of rising cost judge them: a CPU GBDT (L1) over trillions of pairs, a fine-tuned multilingual E5 cross-encoder (L2), and an image-aware reranker (L3) fed from prioritized GPU queues. A bounded union-find splits the graph into single-machine components, and HAC with a learned GBDT merge rule builds high-precision clusters daily, with an LLM judge validated against human labels supplying training data and cached scores reused between builds.