34.1c Google Cloud: A3 (H100), A2 (A100) machine types¶

🌐 Context Introduction¶

Google Cloud Platform (GCP) offers specialized virtual machine (VM) families designed specifically for AI and high-performance computing workloads. The A3 and A2 machine series provide engineers with direct access to NVIDIA's latest GPU architectures — the H100 and A100 — without needing to manage physical hardware. These instances are ideal for training large language models, running inference at scale, and accelerating scientific simulations.

Understanding the differences between A3 (H100) and A2 (A100) helps engineers select the right compute power for their AI workloads while balancing cost, performance, and availability.

⚙️ Overview of A2 (A100) Machine Types¶

The A2 family is built around the NVIDIA A100 Tensor Core GPU, which was designed for AI training and inference, data analytics, and HPC.

Key characteristics: - GPU: NVIDIA A100 (40GB or 80GB HBM2e memory) - Interconnect: NVLink and NVSwitch for multi-GPU communication - Use cases: Large model training, mixed-precision workloads, and inference serving - Networking: Up to 100 Gbps per GPU (with high-bandwidth configurations)

Available configurations: - A2 High-GPU: Up to 16 A100 GPUs per VM - A2 Ultra-GPU: Up to 16 A100 80GB GPUs with higher memory bandwidth - A2 Mega-GPU: 16 A100 80GB GPUs with increased CPU and memory resources

🚀 Overview of A3 (H100) Machine Types¶

The A3 family is GCP's latest GPU-optimized VM series, powered by the NVIDIA H100 Tensor Core GPU. It represents a significant leap in performance for AI workloads.

Key characteristics: - GPU: NVIDIA H100 (80GB HBM3 memory) - Interconnect: 4th-gen NVLink and NVSwitch (900 GB/s per GPU) - Use cases: Large language model (LLM) training, generative AI, and massive-scale inference - Networking: 200 Gbps per GPU with GPUDirect-TCPX

Available configurations: - A3 High: 8 H100 GPUs per VM (standard configuration) - A3 Mega: 8 H100 GPUs with enhanced CPU and memory (coming soon)

📊 Comparison Table: A2 vs A3¶

📊 Visual Representation: GCP a3 (H100) vs. a2 (A100) Machine Series¶

This diagram displays GCP machine families, contrasting A3 (H100) and A2 (A100) compute configurations.

🛠️ When to Choose A2 vs A3¶

Choose A2 (A100) when: - Your models fit within 40GB or 80GB of GPU memory - You need cost-effective training for medium-sized models - You are running inference on established architectures (e.g., BERT, ResNet) - Your workload benefits from up to 16 GPUs in a single VM

Choose A3 (H100) when: - You are training large language models (e.g., GPT-3 scale or larger) - You need the highest possible memory bandwidth for transformer-based models - Your workload requires FP8 or Transformer Engine optimizations - You want to leverage GPUDirect-TCPX for faster distributed training

🕵️ Key Considerations for Engineers¶

Availability and Quotas: - A3 instances are currently available in select regions (e.g., us-central1, europe-west4) - A2 instances have broader regional availability - Both require requesting GPU quota increases through the GCP console

Pricing Model: - Both families support preemptible VMs for cost savings on fault-tolerant workloads - Reserved instances offer significant discounts for long-term commitments - Spot VMs are available for both A2 and A3, but availability varies

Networking Requirements: - For multi-node training, use Titanium network adapters (included with A3) - A2 instances require additional networking configuration for high-bandwidth setups - Both benefit from Google Cloud's Jupiter network fabric for low-latency communication

Storage Considerations: - Use Hyperdisk for high-throughput data loading - Attach local SSDs for temporary checkpoint storage - Consider Cloud Storage FUSE for streaming datasets

📝 Example Workload Mapping¶

Scenario 1: Fine-tuning BERT-Large - Recommended: A2 (A100) with 1-4 GPUs - Reason: Model fits in A100 memory, cost-effective for fine-tuning

Scenario 2: Training GPT-3 175B from scratch - Recommended: A3 (H100) with 8 GPUs per node, multiple nodes - Reason: H100's Transformer Engine and higher bandwidth accelerate training

Scenario 3: Real-time inference for recommendation system - Recommended: A2 (A100) with 1 GPU per VM, autoscaling group - Reason: Lower cost per inference, well-established inference optimization

✅ Summary¶

• A2 (A100) is the reliable workhorse for most AI workloads — great for training and inference with proven performance
• A3 (H100) is the next-generation powerhouse — essential for cutting-edge LLM training and generative AI
• Both families integrate seamlessly with GCP services like Vertex AI, Cloud TPU, and Google Kubernetes Engine (GKE)
• Engineers should evaluate model size, memory requirements, and budget when choosing between them

For the latest pricing, regional availability, and quota limits, always refer to the official Google Cloud GPU documentation.