Tag: ScaleOut

Large Scale Network Driven Design ( Deepseek V3)

Posted on by lechuck park

Deepseek v3 Large-Scale Network Architecture Analysis

This image explains the Multi-Plane Fat-Tree network structure of Deepseek v3.

Core Architecture

1. 8-Plane Architecture

  • Consists of eight independent network channels (highways)
  • Maximizes network bandwidth and distributes traffic for enhanced scalability

2. Fat-Tree Topology

  • Two-layer switch structure:
    • Leaf SW (Leaf Switches): Directly connected to GPUs
    • Spine SW (Spine Switches): Interconnect leaf switches
  • Enables high-speed communication among all nodes (GPUs) while minimizing switch contention

3. GPU/IB NIC Pair

  • Each GPU is paired with a dedicated Network Interface Card (NIC)
  • Each pair is exclusively assigned to one of the eight planes to initiate communication

Communication Methods

NVLink

  • Ultra-high-speed connection between GPUs within the same node
  • Fast data transfer path used for intra-node communication

Cross-plane Traffic

  • Occurs when communication happens between different planes
  • Requires intra-node forwarding through another NIC, PCIe, or NVLink
  • Primary factor that increases latency

Network Optimization Process

The workflow below minimizes latency and prevents network congestion:

  1. Workload Analysis
  2. All to All (analyzing all-to-all communication patterns)
  3. Plane & Layer Set (plane and layer assignment)
  4. Profiling (Hot-path opt K) (hot-path optimization)
  5. Static Routing (Hybrid) (hybrid static routing approach)

Goal: Low latency & no jamming

Scalability

This design is a scale-out network for large-scale distributed training supporting 16,384+ GPUs. Each plane operates independently to maximize overall system throughput.

3-Line Summary

Deepseek v3 uses an 8-plane fat-tree network architecture that connects 16,384+ GPUs through independent communication channels, minimizing contention and maximizing bandwidth. The two-layer switch topology (Spine and Leaf) combined with dedicated GPU-NIC pairs enables efficient traffic distribution across planes. Cross-plane traffic management and hot-path optimization ensure low-latency, high-throughput communication for large-scale AI training.

#DeepseekV3 #FatTreeNetwork #MultiPlane #NetworkArchitecture #ScaleOut #DistributedTraining #AIInfrastructure #GPUCluster #HighPerformanceComputing #NVLink #DataCenterNetworking #LargeScaleAI

With Claude

AI approach

Posted on by lechuck park

Legacy – The Era of Scale-Up

Traditional AI approach showing its limitations:

  • Simple Data: Starting with basic data
  • Simple Data & Logic: Combining data with logic
  • Better Data & Logic: Improving data and logic
  • Complex Data & Logic: Advancing to complex data and logic
  • Near The Limitation: Eventually hitting a fundamental ceiling

This approach gradually increases complexity, but no matter how much it improves, it inevitably runs into fundamental scalability limitations.

AI Works – The Era of Scale-Out

Modern AI transcending the limitations of the legacy approach through a new paradigm:

  • The left side shows the limitations of the old approach
  • The lightbulb icon in the middle represents a paradigm shift (Breakthrough)
  • The large purple box on the right demonstrates a completely different approach:
    • Massive parallel processing of countless “01/10” units (neural network neurons)
    • Horizontal scaling (Scale-Out) instead of sequential complexity increase
    • Fundamentally overcoming the legacy limitations

Key Message

No matter how much you improve the legacy approach, there’s a ceiling. AI breaks through that ceiling with a completely different architecture.

Summary

  • Legacy AI hits fundamental limits by sequentially increasing complexity (Scale-Up)
  • Modern AI uses massive parallel processing architecture to transcend these limitations (Scale-Out)
  • This represents a paradigm shift from incremental improvement to architectural revolution

#AI #MachineLearning #DeepLearning #NeuralNetworks #ScaleOut #Parallelization #AIRevolution #Paradigmshift #LegacyVsModern #AIArchitecture #TechEvolution #ArtificialIntelligence #ScalableAI #DistributedComputing #AIBreakthrough