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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsFacebook’s “new” server and storage designs are best understood as a series of modular platforms, not one current server model. The company’s 2017 designs separated dense hard-drive storage from GPU compute; Meta’s later AI infrastructure applies the same broad idea at larger scale, pairing specialized GPU systems with storage and data paths built for AI workloads. The 2017 hardware is historical, while Meta’s 2024 and 2026 accounts describe later systems and design priorities.
What Facebook’s server designs were meant to do
Facebook launched the Open Compute Project (OCP) in 2011 to share infrastructure designs and invite collaboration. Its initial publications covered servers as well as power, racks, battery backup and building designs. These were reference designs for data-center infrastructure, not consumer servers sold under a Facebook brand. Facebook’s 2011 OCP announcement also framed open hardware as an industry collaboration effort.
In that announcement, Facebook reported that its Prineville data center had an initial power usage effectiveness (PUE) of 1.07, compared with 1.5 at its existing facilities. The company also said the design used 38% less energy to do the same work, reduced infrastructure build-out costs by 24%, and saved more than six pounds of material per server through a simplified design. Those are Facebook’s 2011 figures against its stated baseline, not current or industry-wide results.
How the 2017 platforms divided storage and compute
Facebook’s 2017 fleet refresh included different building blocks for different jobs. Bryce Canyon concentrated on hard-drive capacity; Big Basin separated GPU resources from CPU compute. Tioga Pass and Yosemite v2 added head-node and modular compute options in the same hardware family. This is a description of that 2017 generation, not Meta’s present-day fleet. Meta’s 2017 fleet refresh account describes these designs.
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Bryce Canyon: dense hard-drive storage
Bryce Canyon was a high-density storage chassis supporting 72 hard drives in four Open Rack units. Its modular configurations ranged from a JBOD (a drive enclosure without its own storage-server compute) to a full storage server, and it could accept a single-socket compute card. Meta reported 20% greater HDD density than Open Vault. In a configuration using the Mono Lake compute card, Meta said it offered four times the compute capability of the Honey Badger storage server.
Big Basin: GPU capacity as a separate building block
Big Basin was a JBOG, or “just a bunch of GPUs”: an eight-GPU unit designed to work with an external server head node, keeping CPU compute separate from GPU resources. Its announced configuration used eight NVIDIA Tesla P100 accelerators. Meta said the design raised available GPU memory from 12 GB to 16 GB and enabled training models 30% larger than Big Sur. In Meta’s reported ResNet-50 tests, Big Basin achieved nearly 100% higher throughput than Big Sur. These are the company’s 2017 comparisons, not an independent or current benchmark.
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How the pieces fit
Tioga Pass was a dual-socket server motherboard and head-node option, while Yosemite v2 accepted compute and device cards. Together with Bryce Canyon and Big Basin, they illustrate a modular approach: storage capacity, general-purpose compute and GPU acceleration could be configured as distinct components rather than treated as a single fixed server.
How Meta’s AI infrastructure changed the scale and workload
Meta’s infrastructure approach extends beyond individual servers. In a 2023 overview, infrastructure leader Santosh Janardhan described the company’s scope: “We design, build and operate everything — from the data centers to the server hardware to the mechanical systems that keep everything running.” Meta said it can place GPUs, CPUs, networking and storage together when a workload benefits from that arrangement. Meta’s infrastructure overview appeared in May 2023 and the page also records an April 2025 update.
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In March 2024, Meta described two large generative-AI clusters based on Grand Teton, its in-house-designed GPU platform contributed to OCP. The clusters used either RoCE Ethernet or NVIDIA InfiniBand, with 400 Gbps endpoints. Meta said both had been used for large GenAI workloads, including Llama 3 training on the RoCE cluster. Meta’s 2024 infrastructure account describes the cluster designs.
The storage systems supporting those workloads used YV3 Sierra Point servers with high-capacity E1.S SSDs. Meta described two access approaches: a home-grown FUSE API backed by Tectonic and optimized for flash, and a Hammerspace parallel NFS deployment for interactive workflows. Meta presented these choices as a balance among throughput, rack count and power efficiency. The account does not establish that one storage arrangement is universally better; the workload and operating constraints matter.
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How Meta describes AI storage in 2026
In a July 2026 engineering account, Meta described Tectonic as the horizontally scalable block layer beneath object storage, file systems and block-device interfaces. The company said Tectonic uses erasure coding, supports HDD and flash tiers, and organizes data into hot, warm and cold classes. Meta characterized its environment as hundreds of exabyte-scale storage clusters; those scale terms are Meta’s description, not an independently audited count. Meta’s AI storage account details the newer design.
Shortening the path from stored data to GPUs
Meta said older BLOB storage paths could involve numerous metadata layers, sometimes leading to latencies of hundreds of milliseconds. Its redesigned path uses a unified metadata schema for O(1) path lookup, removes a data-plane proxy so a client SDK streams data directly from storage, and places regional BLOB storage near GPU clusters. The aim is to reduce delays between data retrieval and GPU work.
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Caching and prefetching for training jobs
The design uses spare GPU-host memory to cache commonly read data and a distributed metadata cache for read plans. Meta reported an average 80% hit rate for the distributed data cache and 1–2 ms access to the read-plan cache in its 2026 account; these are company-reported figures, not independent benchmarks.
For research workflows, Meta described a hierarchy spanning host memory and flash, regional flash, and global BLOB storage. Dataloaders can prefetch batches, and an explicit prefetch API can hydrate data ahead of use. Configurable TTL and LRU policies govern regional-cache eviction. This arrangement lets teams trade some consistency in performance for quicker access to datasets that remain remotely stored.
What the design progression means
The useful through-line is specialization and modularity, rather than a claim that one Facebook server design evolved unchanged into Meta’s AI fleet. Bryce Canyon addressed dense HDD capacity; Big Basin made GPU resources independently scalable; Meta’s later clusters combine GPU platforms, network choices and storage systems around AI training and research needs. In the newer storage account, the emphasis shifts from capacity alone toward reducing latency and keeping data close enough to feed GPUs effectively.
- For dense, economical storage, the 2017 Bryce Canyon example prioritized many HDDs per rack unit.
- For accelerator-heavy compute, Big Basin separated GPUs from the external CPU head node; Meta’s 2024 Grand Teton clusters represent a later platform generation.
- For AI data pipelines, Meta’s 2024 and 2026 accounts emphasize flash, regional placement, caching and prefetching alongside durable, globally stored data.
The sources describe different generations and workloads, not a controlled, apples-to-apples comparison of performance, cost, rack footprint or power across all the systems.
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