Comparison of AI accelerators
posted: 01-Oct-2024 & updated: 10-Oct-2024
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Compare between AI accelerators
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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
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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.
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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.
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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.
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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.
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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).
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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.
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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.
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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?