Meta architecture: how Meta scaled (7 case studies)
The Meta write-ups we track cover TAO, Facebook's geo-distributed graph cache over sharded MySQL, which serves more than a billion reads per second; ZippyDB, a distributed key-value store on RocksDB with shard management and configurable consistency; the 2026 World Cup, when WhatsApp set a record of more than 30 million messages per second; and the hardware, networking and reliability work behind training large language models on very large GPU clusters.
We track 7 public engineering write-ups by Meta, published between 2013 and 2026. Techniques that come up more than once: distributed training (3), hardware reliability (2) and networking (2). Stack mentioned most: GPUs (2) and PyTorch (2). 4 of the 7 state concrete scale numbers, for example "gigawatt-scale supercluster". Each card below links to our short summary and the original post.
Tech used in Meta's scaling write-ups
Meta scaling timeline: every story, summarized
- 2013-06-25
TAO: The power of the graph
TAO is Facebook's geo-distributed graph cache over sharded MySQL, serving the social graph at more than a billion reads per second.
- 2021-08-06
ZippyDB: Facebook's key value store
Meta's general-purpose distributed key-value store built on RocksDB, with shard management and configurable consistency.
- 2024-06-12
How Meta trains large language models at scale
Meta covers the hardware, networking and reliability work needed to train LLMs across very large GPU clusters.
- 2026-10-12
Shipping Media Over QUIC at Meta Scale for Instagram Live Ingest
Instagram Live ingest moved from Meta's in-house RUSH protocol to the open IETF Media over QUIC standard on iOS and Android. Edge endpoints translate MoQ frames into the RUSH format, so the backend did not change. Server-side routing let the experiment resize in minutes, the transport falls back to TCP when UDP is blocked, and the launch matched RUSH on quality and reliability.
- 2026-10-13
Meta's Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI
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.
- 2026-10-15
2026 World Cup: Breaking Records, Spiky Charts and Reliability at Scale
How Meta absorbed the 2026 World Cup, when WhatsApp set a record of more than 30 million messages per second during the final. Pre-provisioned capacity buffers were the most effective lever, alongside country-level shedding of non-critical load, load-testing and resizing 100+ services, forecasting each region's peak separately, and no deploy freeze.
- 2026-10-19
Inside Prometheus: Meta's Journey to a Gigawatt-Scale Supercluster
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.
FAQ
What is Facebook TAO?
TAO is Facebook's geo-distributed graph cache over sharded MySQL (with Memcache in its stack). Meta's 2013 post says it serves the social graph at more than a billion reads per second and millions of writes per second.
How did WhatsApp handle the 2026 World Cup?
Meta's 2026 post says pre-provisioned capacity buffers were the most effective lever, alongside country-level shedding of non-critical load, load-testing and resizing 100+ services, forecasting each region's peak separately, and no deploy freeze. WhatsApp set a record of more than 30 million messages per second during the final.
What databases does Meta use?
Two of the four Meta posts we track are about data stores: TAO caches the social graph over sharded MySQL, and ZippyDB is a general-purpose key-value store built on RocksDB and Paxos, with shard management and tunable consistency.