Design Large Model-Weight Distribution to GPU Workers

Scenario

Modern frontier LLMs require weight files ranging from 100 GB to over 500 GB per model checkpoint. In high-scale inference and fine-tuning fleets, thousands of GPU worker nodes must be updated with new model versions without saturating data center backbones, running out of local disk/host memory, or causing downtime during switchovers.

You are tasked with designing the model artifact distribution and deployment system for a fleet of 10,000 GPU worker hosts across multiple availability zones and data centers.

Requirements

Functional

• Artifact Packaging & Integrity: Ingest immutable model weights, chunk them, and verify end-to-end data integrity before exposing weights to the runtime.
• Distribution Mechanism: Reliably distribute weights up to 500 GB to 10,000 GPU workers within a target SLA (e.g., under 20 minutes across the cluster).
• Worker Lifecycle & Safe Activation: Coordinate pre-warming (fetching to local NVMe/host RAM), health checking, and coordinated activation (traffic switch) across nodes.
• Rollback & Failure Recovery: Support instant rollbacks to the previous stable version and resilient handling of stalled, severed, or slow node transfers without holding up cluster-wide rollouts.
• Disk & Resource Management: Manage worker NVMe capacity limits, eviction policies for old versions, and I/O contention with active inference workloads.

Non-Functional & Scale

• Fleet Scale: 10,000 GPU hosts grouped into racks (typically 8–16 hosts per rack) and data centers.
• Artifact Size: 100 GB to 1 TB per model artifact; multiple concurrent models may reside on disk.
• Network Efficiency: Avoid choking top-of-rack (ToR) switches and central storage egress.
• Availability & Safety: Prevent any single node from running with corrupted, mismatched, or partially written weight files.

Key Areas to Cover

1. Data Ingestion & Storage Architecture: How weights are prepared, split, and stored in central storage (e.g., chunking, content-addressable storage, manifest generation).
2. Distribution Topology: P2P (BitTorrent), hierarchical tree distribution, or rack-level cache daemons to reduce cross-rack and cross-DC egress.
3. Host Agent & Activation: How the local agent handles chunks, verifies Merkle trees/checksums, stages weights to disk/RAM, and executes atomic cutovers.
4. Rollout Orchestration: Fleet deployment strategies (canary, rolling batches), dynamic transfer throttling, stalled-worker detection, and zero-overhead rollbacks.