Sunghee Yun

Co-founder & CTO @ Erudio Bio, Inc., KFAS-Salzburg Global Leadership Initiative Fellow @ Salzburg Global Seminar, Adjunct Professor @ Sogang University, Advisory Professor @ DGIST

3 minute read

posted: 01-Oct-2024 & updated: 10-Oct-2024

question

Compare between AI accelerators

answer

Here’s a comparison of some of the major AI hardware solutions developed by big tech companies:

Nvidia H100 (Hopper)

  • Architecture - Nvidia Hopper
  • Process - 4nm
  • Memory - Up to 80 GB HBM3
  • Performance - Over 700 TFLOPs for AI tasks
  • Key Features
    • Specialized tensor cores for AI workloads, optimized for matrix operations.
    • High memory bandwidth, crucial for large-scale AI models.
    • MIG (Multi-Instance GPU) to partition the GPU for multi-tasking.
    • NVLink interconnect for faster communication between multiple GPUs.
  • Best For - Deep learning models, large-scale AI training, inference tasks.

Intel Gaudi2

  • Architecture - Gaudi (custom designed by Habana Labs, acquired by Intel)
  • Process - 7nm
  • Memory - 96 GB HBM2e
  • Performance - 2x speedup compared to Gaudi1, approx. 2 TFLOPS for general computing but much higher for AI workloads.
  • Key Features
    • Purpose-built for AI, with integrated RoCE (RDMA over Converged Ethernet) for scalability.
    • Efficient for training transformer-based models.
    • Competes with Nvidia’s GPUs on performance-per-dollar.
    • Native support for popular frameworks like PyTorch and TensorFlow.
  • Best For - Cost-effective AI training, large-scale AI deployments.

AMD MI300X

  • Architecture - CDNA 3 (Compute DNA, AMD's architecture focused on compute acceleration)
  • Process - 5nm
  • Memory - 128 GB HBM3 (highest among these)
  • Performance - ~4.9 TFLOPs for FP64, targeting data center AI training.
  • Key Features
    • Primarily designed for AI inference and training.
    • Integrated CPU and GPU design (APU), reducing latency.
    • High memory capacity, designed for very large models and datasets.
    • Seamless integration with the ROCm (Radeon Open Compute) software ecosystem.
  • Best For - Large-scale AI models, memory-intensive AI applications.

Amazon Trainium

  • Architecture - Custom Amazon architecture
  • Process - Not disclosed
  • Memory - Not specified, but optimized for use in AWS cloud.
  • Performance - Delivers up to 2x higher throughput for AI models compared to AWS’s other offerings like Nvidia GPUs.
  • Key Features
    • Amazon-designed specifically for cloud AI tasks.
    • Deep integration with AWS infrastructure and SageMaker for model training.
    • Scales efficiently for distributed training tasks.
  • Best For - Cost-effective, scalable AI workloads in cloud environments (especially if using AWS).

Google TPU v4

  • Architecture - Tensor Processing Unit (TPU)
  • Process - Not disclosed
  • Memory - 128 GB HBM2e
  • Performance - Around 275 TFLOPs for AI tasks.
  • Key Features
    • Specifically designed for Google’s AI services.
    • Built for extreme scalability with pod-based systems that combine hundreds of TPUs.
    • Optimized for TensorFlow and JAX, though increasingly compatible with PyTorch.
    • Used extensively in Google’s AI operations (search, cloud AI, etc.).
  • Best For - AI tasks at scale, especially for organizations already within Google Cloud.

Apple Neural Engine (ANE)

  • Architecture - Part of Apple Silicon (M-series chips)
  • Process - 5nm
  • Memory - Integrated within M1/M2 architecture
  • Performance - Focused on 15.8 trillion operations per second (TOPS).
  • Key Features
    • Optimized for on-device AI tasks like image recognition, natural language processing, etc.
    • Dedicated AI acceleration without needing external GPU resources.
    • Integrated with Apple's machine learning framework, Core ML.
  • Best For - On-device AI tasks, AR/VR applications, low-power AI workloads.

Meta (MTIA - Meta Training and Inference Accelerator)

  • Architecture - Custom AI chips (in development)
  • Process - Not disclosed
  • Memory - Expected to be highly scalable
  • Performance - Expected to compete with Nvidia and Google TPUs.
  • Key Features
    • Custom-built to power AI workloads for Meta’s vast social platforms.
    • Expected focus on inference for real-time recommendations, NLP, and image processing.
    • Yet to be publicly deployed, but anticipated to handle massive AI traffic at Meta.
  • Best For - Future AI workloads tied to social media, AR/VR, and metaverse applications.

Microsoft Azure NPUs (Project Brainwave)

  • Architecture - FPGA-based (Field Programmable Gate Array)
  • Process - Not disclosed
  • Memory - Highly scalable via cloud.
  • Performance - Optimized for low-latency AI inference tasks.
  • Key Features
    • Designed for fast, real-time AI inference in the cloud.
    • Azure integration with deep learning frameworks.
    • Customizable FPGAs that can be tailored to specific AI workloads.
  • Best For - Low-latency, real-time AI inference on cloud applications.

summary table

Hardware Best Use Case Performance (TFLOPS) Memory
Nvidia H100 Deep learning models, training ~700 TFLOPS (AI tasks) 80 GB HBM3
Intel Gaudi2 Cost-effective AI training 2x Gaudi1 (~2 TFLOPS) 96 GB HBM2e
AMD MI300X Large models, data center AI ~4.9 TFLOPS (FP64) 128 GB HBM3
Amazon Trainium Scalable cloud AI workloads ~2x higher throughput in AWS Not specified
Google TPU v4 AI tasks at Google scale ~275 TFLOPS (AI tasks) 128 GB HBM2e
Apple Neural Engine On-device AI, low-power AI 15.8 TOPS Integrated (M1/M2)
Meta MTIA Social media, inference tasks Not disclosed (in development) TBD
Microsoft Azure NPU Real-time, low-latency inference FPGA-based, cloud-scalable Scalable (cloud)

Each of these chips has a different specialty, depending on whether you’re looking for cloud AI, on-device AI, or large-scale deep learning tasks. Which aspect of AI hardware are you most interested in for your business?

Updated:

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