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Continuum Knowledge
  • Continuum
  • Data
    • Datasets
      • Pre Training Data
      • Types of Fine Tuning
      • Self Instruct Paper
      • Self-Alignment with Instruction Backtranslation
      • Systematic Evaluation of Instruction-Tuned Large Language Models on Open Datasets
      • Instruction Tuning
      • Instruction Fine Tuning - Alpagasus
      • Less is More For Alignment
      • Enhanced Supervised Fine Tuning
      • Visualising Data using t-SNE
      • UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
      • Training and Evaluation Datasets
      • What is perplexity?
  • MODELS
    • Foundation Models
      • The leaderboard
      • Foundation Models
      • LLama 2 - Analysis
      • Analysis of Llama 3
      • Llama 3.1 series
      • Google Gemini 1.5
      • Platypus: Quick, Cheap, and Powerful Refinement of LLMs
      • Mixtral of Experts
      • Mixture-of-Agents (MoA)
      • Phi 1.5
        • Refining the Art of AI Training: A Deep Dive into Phi 1.5's Innovative Approach
      • Phi 2.0
      • Phi-3 Technical Report
  • Training
    • The Fine Tuning Process
      • Why fine tune?
        • Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?
        • Explanations in Fine Tuning
      • Tokenization
        • Tokenization Is More Than Compression
        • Tokenization - SentencePiece
        • Tokenization explore
        • Tokenizer Choice For LLM Training: Negligible or Crucial?
        • Getting the most out of your tokenizer for pre-training and domain adaptation
        • TokenMonster
      • Parameter Efficient Fine Tuning
        • P-Tuning
          • The Power of Scale for Parameter-Efficient Prompt Tuning
        • Prefix-Tuning: Optimizing Continuous Prompts for Generation
        • Harnessing the Power of PEFT: A Smarter Approach to Fine-tuning Pre-trained Models
        • What is Low-Rank Adaptation (LoRA) - explained by the inventor
        • Low Rank Adaptation (Lora)
        • Practical Tips for Fine-tuning LMs Using LoRA (Low-Rank Adaptation)
        • QLORA: Efficient Finetuning of Quantized LLMs
        • Bits and Bytes
        • The Magic behind Qlora
        • Practical Guide to LoRA: Tips and Tricks for Effective Model Adaptation
        • The quantization constant
        • QLORA: Efficient Finetuning of Quantized Language Models
        • QLORA and Fine-Tuning of Quantized Language Models (LMs)
        • ReLoRA: High-Rank Training Through Low-Rank Updates
        • SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models
        • GaLora: Memory-Efficient LLM Training by Gradient Low-Rank Projection
      • Hyperparameters
        • Batch Size
        • Padding Tokens
        • Mixed precision training
        • FP8 Formats for Deep Learning
        • Floating Point Numbers
        • Batch Size and Model loss
        • Batch Normalisation
        • Rethinking Learning Rate Tuning in the Era of Language Models
        • Sample Packing
        • Gradient accumulation
        • A process for choosing the learning rate
        • Learning Rate Scheduler
        • Checkpoints
        • A Survey on Efficient Training of Transformers
        • Sequence Length Warmup
        • Understanding Training vs. Evaluation Data Splits
        • Cross-entropy loss
        • Weight Decay
        • Optimiser
        • Caching
      • Training Processes
        • Extending the context window
        • PyTorch Fully Sharded Data Parallel (FSDP)
        • Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation
        • YaRN: Efficient Context Window Extension of Large Language Models
        • Sliding Window Attention
        • LongRoPE
        • Reinforcement Learning
        • An introduction to reinforcement learning
        • Reinforcement Learning from Human Feedback (RLHF)
        • Direct Preference Optimization: Your Language Model is Secretly a Reward Model
  • INFERENCE
    • Why is inference important?
      • Grouped Query Attention
      • Key Value Cache
      • Flash Attention
      • Flash Attention 2
      • StreamingLLM
      • Paged Attention and vLLM
      • TensorRT-LLM
      • Torchscript
      • NVIDIA L40S GPU
      • Triton Inference Server - Introduction
      • Triton Inference Server
      • FiDO: Fusion-in-Decoder optimised for stronger performance and faster inference
      • Is PUE a useful measure of data centre performance?
      • SLORA
  • KNOWLEDGE
    • Vector Databases
      • A Comprehensive Survey on Vector Databases
      • Vector database management systems: Fundamental concepts, use-cases, and current challenges
      • Using the Output Embedding to Improve Language Models
      • Decoding Sentence-BERT
      • ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT
      • SimCSE: Simple Contrastive Learning of Sentence Embeddings
      • Questions Are All You Need to Train a Dense Passage Retriever
      • Improving Text Embeddings with Large Language Models
      • Massive Text Embedding Benchmark
      • RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
      • LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
      • Embedding and Fine-Tuning in Neural Language Models
      • Embedding Model Construction
      • Demystifying Embedding Spaces using Large Language Models
      • Fine-Tuning Llama for Multi-Stage Text Retrieval
      • Large Language Model Based Text Augmentation Enhanced Personality Detection Model
      • One Embedder, Any Task: Instruction-Finetuned Text Embeddings
      • Vector Databases are not the only solution
      • Knowledge Graphs
        • Harnessing Knowledge Graphs to Elevate AI: A Technical Exploration
        • Unifying Large Language Models and Knowledge Graphs: A Roadmap
      • Approximate Nearest Neighbor (ANN)
      • High Dimensional Data
      • Principal Component Analysis (PCA)
      • Vector Similarity Search - HNSW
      • FAISS (Facebook AI Similarity Search)
      • Unsupervised Dense Retrievers
    • Retrieval Augmented Generation
      • Retrieval-Augmented Generation for Large Language Models: A Survey
      • Fine-Tuning or Retrieval?
      • Revolutionising Information Retrieval: The Power of RAG in Language Models
      • A Survey on Retrieval-Augmented Text Generation
      • REALM: Retrieval-Augmented Language Model Pre-Training
      • Retrieve Anything To Augment Large Language Models
      • Generate Rather Than Retrieve: Large Language Models Are Strong Context Generators
      • Active Retrieval Augmented Generation
      • DSPy: LM Assertions: Enhancing Language Model Pipelines with Computational Constraints
      • DSPy: Compiling Declarative Language Model Calls
      • DSPy: In-Context Learning for Extreme Multi-Label Classification
      • Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs
      • HYDE: Revolutionising Search with Hypothetical Document Embeddings
      • Enhancing Recommender Systems with Large Language Model Reasoning Graphs
      • Retrieval Augmented Generation (RAG) versus fine tuning
      • RAFT: Adapting Language Model to Domain Specific RAG
      • Summarisation Methods and RAG
      • Lessons Learned on LLM RAG Solutions
      • Stanford: Retrieval Augmented Language Models
      • Overview of RAG Approaches with Vector Databases
      • Mastering Chunking in Retrieval-Augmented Generation (RAG) Systems
    • Semantic Routing
    • Resource Description Framework (RDF)
  • AGENTS
    • What is agency?
      • Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves
      • Types of Agents
      • The risk of AI agency
      • Understanding Personality in Large Language Models: A New Frontier in AI Psychology
      • AI Agents - Reasoning, Planning, and Tool Calling
      • Personality and Brand
      • Agent Interaction via APIs
      • Bridging Minds and Machines: The Legacy of Newell, Shaw, and Simon
      • A Survey on Language Model based Autonomous Agents
      • Large Language Models as Agents
      • AI Reasoning: A Deep Dive into Chain-of-Thought Prompting
      • Enhancing AI Reasoning with Self-Taught Reasoner (STaR)
      • Exploring the Frontier of AI: The "Tree of Thoughts" Framework
      • Toolformer: Revolutionising Language Models with API Integration - An Analysis
      • TaskMatrix.AI: Bridging Foundational AI Models with Specialised Systems for Enhanced Task Completion
      • Unleashing the Power of LLMs in API Integration: The Rise of Gorilla
      • Andrew Ng's presentation on AI agents
      • Making AI accessible with Andrej Karpathy and Stephanie Zhan
  • Regulation and Ethics
    • Regulation and Ethics
      • Privacy
      • Detecting AI Generated content
      • Navigating the IP Maze in AI: The Convergence of Blockchain, Web 3.0, and LLMs
      • Adverse Reactions to generative AI
      • Navigating the Ethical Minefield: The Challenge of Security in Large Language Models
      • Navigating the Uncharted Waters: The Risks of Autonomous AI in Military Decision-Making
  • DISRUPTION
    • Data Architecture
      • What is a data pipeline?
      • What is Reverse ETL?
      • Unstructured Data and Generatve AI
      • Resource Description Framework (RDF)
      • Integrating generative AI with the Semantic Web
    • Search
      • BM25 - Search Engine Ranking Function
      • BERT as a reranking engine
      • BERT and Google
      • Generative Engine Optimisation (GEO)
      • Billion-scale similarity search with GPUs
      • FOLLOWIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
      • Neural Collaborative Filtering
      • Federated Neural Collaborative Filtering
      • Latent Space versus Embedding Space
      • Improving Text Embeddings with Large Language Models
    • Recommendation Engines
      • On Interpretation and Measurement of Soft Attributes for Recommendation
      • A Survey on Large Language Models for Recommendation
      • Model driven recommendation systems
      • Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations
      • Foundation Models for Recommender Systems
      • Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Review
      • AI driven recommendations - harming autonomy?
    • Logging
      • A Taxonomy of Anomalies in Log Data
      • Deeplog
      • LogBERT: Log Anomaly Detection via BERT
      • Experience Report: Deep Learning-based System Log Analysis for Anomaly Detection
      • Log-based Anomaly Detection with Deep Learning: How Far Are We?
      • Deep Learning for Anomaly Detection in Log Data: A Survey
      • LogGPT
      • Adaptive Semantic Gate Networks (ASGNet) for log-based anomaly diagnosis
  • Infrastructure
    • The modern data centre
      • Enhancing Data Centre Efficiency: Strategies to Improve PUE
      • TCO of NVIDIA GPUs and falling barriers to entry
      • Maximising GPU Utilisation with Kubernetes and NVIDIA GPU Operator
      • Data Centres
      • Liquid Cooling
    • Servers and Chips
      • The NVIDIA H100 GPU
      • NVIDIA H100 NVL
      • Lambda Hyperplane 8-H100
      • NVIDIA DGX Servers
      • NVIDIA DGX-2
      • NVIDIA DGX H-100 System
      • NVLink Switch
      • Tensor Cores
      • NVIDIA Grace Hopper Superchip
      • NVIDIA Grace CPU Superchip
      • NVIDIA GB200 NVL72
      • Hopper versus Blackwell
      • HGX: High-Performance GPU Platforms
      • ARM Chips
      • ARM versus x86
      • RISC versus CISC
      • Introduction to RISC-V
    • Networking and Connectivity
      • Infiniband versus Ethernet
      • NVIDIA Quantum InfiniBand
      • PCIe (Peripheral Component Interconnect Express)
      • NVIDIA ConnectX InfiniBand adapters
      • NVMe (Non-Volatile Memory Express)
      • NVMe over Fabrics (NVMe-oF)
      • NVIDIA Spectrum-X
      • NVIDIA GPUDirect
      • Evaluating Modern GPU Interconnect
      • Scalable Hierarchical Aggregation and Reduction Protocol (SHARP)
      • Next-generation networking in AI environments
      • NVIDIA Collective Communications Library (NCCL)
    • Data and Memory
      • NVIDIA BlueField Data Processing Units (DPUs)
      • Remote Direct Memory Access (RDMA)
      • High Bandwidth Memory (HBM3)
      • Flash Memory
      • Model Requirements
      • Calculating GPU memory for serving LLMs
      • Transformer training costs
      • GPU Performance Optimisation
    • Libraries and Complements
      • NVIDIA Base Command
      • NVIDIA AI Enterprise
      • CUDA - NVIDIA GTC 2024 presentation
      • RAPIDs
      • RAFT
    • Vast Data Platform
      • Vast Datastore
      • Vast Database
      • Vast Data Engine
      • DASE (Disaggregated and Shared Everything)
      • Dremio and VAST Data
    • Storage
      • WEKA: A High-Performance Storage Solution for AI Workloads
      • Introduction to NVIDIA GPUDirect Storage (GDS)
        • GDS cuFile API
      • NVIDIA Magnum IO GPUDirect Storage (GDS)
      • Vectors in Memory
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Copyright Continuum Labs - 2023

On this page
  • PCE Switches versus NVLINK
  • Introduction of NVLink
  • NVLINK - Key Points
  • Development History
  • Comparison with InfiniBand

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  1. Infrastructure
  2. Servers and Chips

NVLink Switch

Rapid Communication between GPUs

PreviousNVIDIA DGX H-100 SystemNextTensor Cores

Last updated 11 months ago

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NVIDIA NVLink is a high-speed, direct interconnect technology designed to enable fast communication between GPUs within a server or across multiple servers.

By providing CUDA acceleration across different layers, NVLink reduces communication-related network overhead.

NVLink vastly improves scalability for larger multi-GPU systems.

A single NVIDIA Blackwell Tensor Core GPU supports up to 18 NVLink 100 gigabyte-per-second (GB/s) connections for a total bandwidth of 1.8 terabytes per second (TB/s)— 2 times more bandwidth than the previous generation and over 14 times the bandwidth of PCIe Gen5.

Server platforms like the take advantage of this technology to deliver greater scalability for today’s most complex large models.

NVLink in NVIDIA H100 increases inter-GPU communication bandwidth 1.5 times compared to the previous generation, so researchers can use larger, more sophisticated applications to solve more complex problems.

PCE Switches versus NVLINK

PCIe (Peripheral Component Interconnect Express) switches act as a central hub that allow multiple PCIe devices, such as GPUs, to communicate with each other and with the host system.

Before NVLink technology was introduced (before 2014), GPUs had to be interconnected through a PCIe switch.

PCIe stood as the traditional backbone for GPU interconnectivity in servers. While it offers lower bandwidth compared to NVLink, PCIe's strength lies in its flexibility and broad compatibility. It caters to a diverse range of server architectures, making it a versatile choice for many AI applications, especially where the inter-GPU communication load is moderate.

However, PCIe switches have several limitations that can impact the performance of multi-GPU systems:

Bandwidth limitations

PCIe switches have limited bandwidth compared to direct GPU-to-GPU connections like NVLink.

The total bandwidth available is shared among all the connected devices, which can lead to bottlenecks when multiple GPUs are transferring large amounts of data simultaneously.

Latency

PCIe switches introduce additional latency due to the time required for data to pass through the switch. This latency can accumulate when multiple switches are used in a system, resulting in slower communication between GPUs.

Scalability

As the number of GPUs in a system increases, the number of PCIe switches required also increases, leading to more complex topologies.

This complexity can make it challenging to maintain optimal performance and can introduce additional points of failure.

Power consumption

PCIe switches consume additional power, which can be a concern in large-scale systems with many GPUs. The increased power consumption can lead to higher operating costs and may require more advanced cooling solutions.

Introduction of NVLink

To address these limitations, NVIDIA developed NVLink, which provides higher bandwidth and lower latency connections between GPUs. NVLink enables direct GPU-to-GPU communication, bypassing the need for PCIe switches.

This direct connection allows for faster data transfer and reduces the latency associated with traditional PCIe switches.

In summary, while PCIe switches play a role in connecting PCIe devices, they have limitations in terms of bandwidth, latency, scalability, power consumption, and cost.

NVLink addresses these limitations by providing high-speed, direct GPU-to-GPU connections, enabling faster and more efficient communication between GPUs in multi-GPU systems.

NVLINK - Key Points

Purpose

NVLink enables high-speed, direct communication between GPUs, reducing the overhead associated with traditional networks and allowing GPUs to work together more efficiently.

NVIDIA has introduced significant improvements to its NVLink interconnect technology.

The fifth-generation NVLink can support up to 576 GPUs concurrently, providing high-speed, low-latency communication between GPUs within a server or across a data centre.

Bandwidth

NVLink offers significantly higher bandwidth compared to PCIe. The fourth-generation NVLink provides 112Gbps per lane, which is three times faster than PCIe Gen5.

NVSwitch Chip

The NVSwitch chip is a physical chip that connects multiple GPUs using high-speed NVLink interfaces.

NVLink Servers

These servers are widely used in scientific computing, AI, big data processing, and data centres.

NVLink Switch

The NVLink Switch is a critical component of this ecosystem that enables the creation of a dedicated NVLink network for GPU-to-GPU communication.

With a bandwidth of 1.8 terabytes per second per GPU, NVLink Switch offers over 14 times the bandwidth of PCIe Gen 5.

This high-speed interconnect is crucial for achieving optimal performance in AI workloads and large-scale GPU deployments.

NVLink Network

The NVLink network is created by connecting multiple NVLink GPU servers using NVSwitch physical switches.

This network is independent of IP Ethernet and dedicated to GPU service, providing high-speed communication bandwidth and efficiency between GPUs.

Development History

NVLink has evolved alongside GPU architecture, progressing from NVLink1 for P100 to NVLink4 for H100, as depicted in the figure.

The key difference among NVLink 1.0, NVLink 2.0, NVLink 3.0, and NVLink 4.0 lies in the connection method, bandwidth, and performance.

Comparison with InfiniBand

NVLink Network is a proprietary technology designed specifically for high-speed direct connections between GPUs, while InfiniBand Network is an open-standard networking technology used in high performance computing clusters and large-scale data centres.

InfiniBand has been the go-to technology for high-performance computing applications, including AI, due to its low latency and high bandwidth.

However, Ethernet has been catching up in terms of speed and is significantly cheaper. Infiniband is relatively difficult to configure, maintain, and scale, and it requires specialised hardware, making expansion more costly than Ethernet.

Ethernet outperforms InfiniBand in terms of performance, cost-effectiveness, and openness. Ethernet's 10% performance advantage can lead to significant cost savings in large-scale AI/ML infrastructures.

NVIDIA, the main vendor for InfiniBand, is slowly migrating to an Ethernet-based framework, which could impact the future support for InfiniBand.

NVLink Network offers higher bandwidth and lower latency between GPUs compared to InfiniBand.

In summary, NVIDIA NVLink is a technology that enables fast, direct communication between GPUs, enhancing performance and enabling efficient parallel processing in HPC and AI applications.

NVLINK addresses the limitations of traditional by providing higher bandwidth and lower latency connections between GPUs.

The third-generation NVSwitch chip (NVSwitch3) can interconnect each pair of GPUs at 900 GB/s and includes the for aggregating and updating computation results across multiple GPU units.

NVLink servers, such as series or OEM HGX servers, incorporate NVLink and NVSwitch technologies to provide GPU interconnectivity, scalability, and HPC capabilities.

SHARP function
NVIDIA's DGX
PCIe switches
GB200 NVL72
Now in its fourth generation, NVLink connects host and accelerated processors at rates up to 900 gigabytes per second (GB/s)
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