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PATTERN Cited by 1 source

Distributed data cache on GPU hosts

Pattern

Leverage spare memory on GPU hosts as a distributed peer-to-peer data cache for frequently and concurrently accessed data, integrated directly into the storage client SDK.

Problem

AI workloads access data concurrently across hundreds of GPUs. Model weights and popular dataset shards are "hot." Events like GPU restarts trigger sharp traffic spikes to backend storage.

Solution

Reuse the existing Owl content-distribution peer subsystem by integrating its peers directly into the BLOB-storage client SDK. All data access routes through this distributed cache layer before falling through to backend storage.

Results at Meta

  • 80% average cache hit rate across workloads
  • Absorbs spikes and reduces I/O requirements from backend storage
  • Solves the metadata hot-shard problem (hot data served from peer memory)
  • Improves p50 and p99 latencies by serving from memory
  • Zero additional infrastructure — uses spare memory already present on GPU hosts

(Source: sources/2026-07-01-meta-ai-storage-blueprint-at-scale)

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