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  • 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
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      • 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
  • Architecture Diagram
  • Here's a detailed explanation of the architecture
  • Grace CPU
  • Hopper GPU
  • Ecosystem and software support
  • Key features of the Grace Hopper Superchip architecture
  • Breaking into the data centre CPU market

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  1. Infrastructure
  2. Servers and Chips

NVIDIA Grace Hopper Superchip

PreviousTensor CoresNextNVIDIA Grace CPU Superchip

Last updated 11 months ago

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NVIDIA's creation of the Grace Hopper Superchip architecture is a strategic move to expand its presence in the data centre market.

Traditionally, the data centre CPU market has been dominated by x86-based processors from Intel and AMD. By offering a high-performance, energy-efficient ARM-based CPU solution, NVIDIA aims to compete against incumbent x86-based technologies from Intel and AMD.

By introducing the Grace CPU, NVIDIA aims to challenge this dominance and offer an alternative ARM-based solution specifically designed for data centre workloads.

The integration of the Grace CPU with NVIDIA's GPUs through the NVLink interconnect creates a compelling platform for AI, HPC, and data analytics workloads.

The high-bandwidth, low-latency connection between the Grace CPU and Hopper GPU enables efficient data transfer and communication, optimising overall system performance.

By offering a tightly integrated CPU-GPU solution, NVIDIA aims to provide a compelling platform for AI, high-performance computing, and data analytics workloads.

Also importantly, the Grace CPU's focus on energy efficiency and high memory bandwidth aligns with the growing demand for power-efficient and high-performance computing in data centres.

Architecture Diagram

The diagram below illustrates the architecture of the NVIDIA Grace Hopper Superchip, which combines an NVIDIA Hopper GPU with the new NVIDIA Grace CPU connected via a high-speed, low-latency NVLink interconnect.

Here's a detailed explanation of the architecture

Grace CPU

  • The Grace CPU is NVIDIA's first data centre CPU, featuring 72 Arm Neoverse V2 cores, which are ARM's highest-performance core design. ARM Neoverse is a family of IP cores specifically designed for server and infrastructure workloads.

  • It has 512GB of LPDDR5X memory, providing energy efficiency and high bandwidth of 546 GB/s per CPU.

  • LPDDR (Low Power Double Data Rate) is a type of memory technology commonly used in mobile devices and embedded systems.

  • LPDDR5X is the latest generation of LPDDR memory, offering higher bandwidth and improved energy efficiency compared to previous generation

  • Compared to traditional 8-channel DDR5 designs, Grace CPU's LPDDR5X memory offers 53% more bandwidth while consuming less power.

NVIDIA's decision to use ARM-based cores and LPDDR5X memory in the Grace CPU represents a departure from traditional x86-based CPUs and DDR memory designs commonly used in data centres.

Hopper GPU

  • The Hopper GPU is NVIDIA's 9th generation data centre GPU

  • It features 96GB of HBM3 memory, a first in the market, providing 3 TB/s of memory bandwidth.

  • Hopper has an increased number of Streaming Multiprocessors, higher frequency, and new 4th Generation Tensor Cores.

  • The new Transformer Engine in Hopper enables up to six times higher throughput compared to the previous generation A100 GPU.

NVLink Interconnect

  • The Grace CPU and Hopper GPU are connected via a high-speed, low-latency NVLink interconnect.

  • The NVLink provides 900 GB/s of bidirectional bandwidth between the CPU and GPU.

  • This high-bandwidth, low-latency connection enables efficient data transfer and communication between the CPU and GPU, optimising performance for demanding workloads.

Memory Configuration

  • The Grace Hopper Superchip has a total of 608GB of memory, consisting of 512GB LPDDR5X for the Grace CPU and 96GB HBM3 for the Hopper GPU.

  • The CPU's LPDDR5X memory offers 546 GB/s of bandwidth per CPU, while the GPU's HBM3 memory provides 3 TB/s of bandwidth.

  • This memory configuration ensures high-speed access to data for both the CPU and GPU, enabling efficient processing of large datasets and complex workloads.

Ecosystem and software support

  • NVIDIA's extensive software ecosystem, including CUDA, cuDNN, and TensorRT, can be leveraged to optimise workloads running on the Grace CPU and Grace Hopper Superchip.

  • NVIDIA's existing partnerships and collaborations with key players in the data centre industry can help drive adoption and support for the Grace CPU.

  • However, the success of the Grace CPU will also depend on the broader adoption of ARM-based solutions in the data centre market and the availability of software optimised for ARM architectures.

Key features of the Grace Hopper Superchip architecture

High-performance CPU: The 72-core Grace CPU with Arm Neoverse V2 cores delivers exceptional performance for data centre workloads.

Energy-efficient memory: The use of LPDDR5X memory in the Grace CPU provides high bandwidth while consuming less power compared to traditional DDR5 designs.

Cutting-edge GPU: The Hopper GPU brings advancements such as HBM3 memory, increased Streaming Multiprocessors, higher frequency, and new Tensor Cores, enabling faster AI processing.

Fast interconnect: The high-bandwidth, low-latency NVLink interconnect ensures efficient data transfer between the CPU and GPU, optimizing overall system performance.

Huge memory capacity: With a total of 608GB of memory (512GB LPDDR5X + 96GB HBM3), the Grace Hopper Superchip can handle large datasets and memory-intensive workloads.

The NVIDIA Grace Hopper Superchip architecture combines the strengths of the Grace CPU and Hopper GPU to deliver exceptional performance, energy efficiency, and high-speed memory access.

This powerful combination makes it well-suited for demanding data centre workloads, particularly in the areas of AI, high-performance computing, and data analytics.

NVIDIA's creation of the Grace CPU and the Grace Hopper Superchip architecture is indeed a strategic move to strengthen its position in the data centre market. Here's an analysis of NVIDIA's motivation and the competitive landscape:

Breaking into the data centre CPU market

By introducing the Grace CPU and the Grace Hopper Superchip architecture, NVIDIA aims to strengthen its position in the data centre market, challenging the dominance of x86-based processors from Intel and AMD.

The ARM-based Grace CPU offers an alternative solution designed specifically for data centre workloads, providing better performance per watt and higher memory bandwidth compared to incumbent technologies.

Overall, the NVIDIA Grace Hopper Superchip architecture represents a significant advancement in data centre computing, combining high-performance CPU and GPU capabilities with energy efficiency and fast memory access to tackle the most demanding workloads in AI, HPC, and data analytics.

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