GCP A4 Instances with NVIDIA H100 80GB Now GA — 67% Cheaper Than AWS p5 for the Same GPU Hardware

What is the GCP A4 Instance?

The GCP A4 instance family (a4-highgpu-8g) is Google Cloud's flagship AI training instance, featuring 8× NVIDIA H100 SXM 80GB GPUs interconnected via NVLink at 900 GB/s bandwidth. The H100 represents a 3-6× improvement in transformer model training throughput versus the A100 GPU, making it the current gold standard for large language model training. Each A4 instance includes 192 vCPU, 1.4 TB of RAM and 48 TB of local NVMe storage.

What Changed?

GCP A4 instances with 8× H100 80GB GPUs are now generally available in us-central1, us-east4, europe-west4, and asia-southeast1 (Singapore). Pricing: $32.77/hr on-demand, $22.94/hr with 1-year CUD (Committed Use Discount), and $16.39/hr with 3-year CUD. Spot pricing is approximately $9.83/hr — subject to preemption with 30-second notice. GCP Sustained Use Discounts also apply for on-demand instances running more than 25% of the month.

Why Does This Matter?

The price comparison against AWS is dramatic: AWS p5.48xlarge (8× H100 80GB) costs $98.32/hr on-demand — GCP A4 at $32.77/hr is 67% cheaper for identical GPU hardware. Even comparing 3-year committed pricing: AWS p5 3yr Reserved is approximately $39.33/hr vs GCP A4 3yr CUD at $16.39/hr — GCP is still 58% cheaper on committed pricing. For a team running continuous model training on 4 instances: annual cost on AWS = $3.4M, annual cost on GCP = $1.1M.

How to Use It

A4 instances support CUDA 12.x natively, with pre-built Deep Learning VM images available via GCP Marketplace including PyTorch, TensorFlow, and JAX with H100-optimised libraries. For maximum H100 utilisation, use FlashAttention-2, gradient checkpointing, and mixed-precision training (bfloat16). For Spot instances, implement checkpointing every 5-10 minutes using Google Cloud Storage — resume from checkpoint on interruption. Use Vertex AI Training Jobs for managed orchestration with automatic Spot fallback to on-demand.

Who Should Act Now

Any team training models with 13B+ parameters should evaluate A4 immediately. The H100 performance advantage over A100 is most pronounced for transformer architectures using attention mechanisms — the H100 Transformer Engine with FP8 precision delivers up to 6× faster attention computation vs A100. Teams currently on GCP p4d (A100) should benchmark H100 training speed — the combination of faster training and lower price typically results in 70-80% lower cost-per-training-run on A4 vs p4d.