Google Cloud TPU v8 vs AWS Trainium2 vs NVIDIA H100: Best AI Training Cloud for APAC Enterprises 2026

Google Cloud's newly announced TPU Trillium chips (v8t for training, v8i for inference) have reset the competitive landscape for AI training infrastructure. With a stated 121 exaflops of training capacity per pod, Trillium is Google's most direct answer yet to the NVIDIA H100/H200 dominance that has defined enterprise AI buildouts since 2023. At the same time, AWS continues to scale its custom Trainium2 silicon, and the open GPU cloud market (CoreWeave, Lambda Labs, Vast.ai) offers spot H100s at prices that hyperscalers struggle to match.

If you're an APAC enterprise deciding where to run your next LLM pre-training run or large-scale fine-tuning job, the choice between these three paths carries real cost and latency consequences. This article breaks down what the data actually shows — no vendor spin.

The Contenders: What Each Platform Actually Offers

Google Cloud TPU Trillium (v8t / v8i)

Announced in mid-2025, TPU Trillium is the sixth generation of Google's custom AI silicon. Key published specs:

• 121 exaflops aggregate training capacity per TPU pod (Google's stated figure)
• v8t variant optimised for training (higher memory bandwidth); v8i variant optimised for inference (lower latency per token)
• Native integration with Vertex AI, JAX, and PyTorch XLA
• Available in Google's us-central1, us-east4, and asia-southeast1 (Singapore) regions — APAC coverage is still narrower than AWS
• Pricing not yet publicly listed for Trillium; TPU v5e pods run approximately $2.20–$2.80/chip-hour on on-demand, with 1-year CUDs bringing that down ~30%

Google's own production data point is notable: GCP maintained ~1.6 billion tokens/month average throughput in Q1 2025 across its AI services — evidence that the underlying infrastructure handles hyperscale workloads. Gemini 3.1 Pro's 1M-token context window, now in preview, also runs on this same TPU stack, which gives you a real-world reference for what the hardware can sustain.

AWS Trainium2

AWS Trainium2, available via Trn2 instances, is Amazon's second-generation custom AI training chip. Published specs and pricing data:

• trn2.48xlarge: 16 Trainium2 chips, 1.5 TB memory, ~$35/hour on-demand (us-east-1)
• NeuronSDK supports PyTorch natively; JAX support is partial and community-maintained
• AWS claims 2× price-performance improvement over Trainium1 for transformer training — but independent benchmarks on specific model architectures vary significantly
• Strong APAC presence: ap-southeast-1 (Singapore), ap-northeast-1 (Tokyo), ap-northeast-2 (Seoul) all have Trn2 availability
• Tight integration with SageMaker, S3, and AWS's security/compliance stack (important for regulated APAC industries)

NVIDIA H100 / H200 (GPU Cloud & Hyperscaler)

H100 SXM5 remains the de facto benchmark for LLM training. Reference data points:

• GCP A3 Mega (H100 SXM5 × 8): ~$32–$40/hour on-demand; ~$22–$26/hour on 1-year CUD
• AWS p5.48xlarge (H100 SXM5 × 8): ~$98/hour on-demand — among the most expensive options; 1-year Reserved brings it to ~$65/hour
• Spot/interruptible H100 from third-party clouds (CoreWeave APAC, Lambda Labs): $2.10–$2.80/GPU-hour for spot, $3.20–$4.50/GPU-hour reserved — substantially cheaper for fault-tolerant training jobs
• Broad APAC availability: Singapore, Tokyo, Sydney, Mumbai nodes exist across multiple providers
• Framework support is universal — PyTorch, JAX, TensorFlow, all CUDA libraries work out of the box

Head-to-Head: 4 Dimensions That Actually Matter for APAC AI Training

1. Raw Training Throughput

Google's 121 exaflops claim is a pod-level aggregate — meaningful for organisations that can consume an entire TPU pod. For most APAC enterprises running 7B–70B parameter fine-tuning jobs rather than frontier pre-training, the relevant unit is per-chip throughput. TPU v5e independent benchmarks (the last publicly verified generation) show roughly 197 TFLOPs (bfloat16) per chip. H100 SXM5 delivers approximately 989 TFLOPs (FP16) per chip — roughly 5× more per unit, though TPU pods achieve efficiency gains through their proprietary interconnect fabric (ICI) that reduces communication overhead at scale.

Verdict: For jobs that fill a TPU pod, GCP Trillium is highly competitive. For smaller fine-tuning runs below 256 chips, H100 clusters remain more flexible and often faster per dollar.

2. Total Cost of Training: A 70B LLM Fine-Tuning Reference

Using a reference workload of fine-tuning a 70B parameter model for 10 billion tokens (typical instruction-tuning scale):

• H100 × 64 (GCP A3 Mega, 1-year CUD): ~$22/hr × 8 nodes × ~18 hours ≈ ~$3,170
• Trainium2 trn2.48xlarge × 4 (1-year Reserved): ~$65/hr × 4 nodes × ~22 hours ≈ ~$5,720 (Trainium2 is slower per chip but AWS claims better price-performance at scale with NeuronSDK optimisation)
• Spot H100 × 64 (third-party APAC cloud): ~$3.00/GPU-hr × 64 GPUs × ~18 hours ≈ ~$3,456 — but with interruption risk
• TPU v8t (Trillium, estimated based on v5e CUD pricing trajectories): Pricing not yet confirmed; early access customers should request committed-use quotes directly from Google

Key takeaway: AWS Trainium2 is not necessarily cheaper than H100 on GCP when you account for the speed difference. The NeuronSDK lock-in (recompiling models for Neuron) adds engineering overhead that has a real cost for teams without dedicated MLOps resources.

3. APAC Regional Availability & Latency

For APAC enterprises, data residency and latency to training data sources matter:

• AWS has the broadest APAC footprint with Trainium2 available in Singapore, Tokyo, and Seoul — strong choice for teams with data gravity in AWS S3 buckets
• GCP Trillium is currently in preview with limited regional availability; asia-southeast1 (Singapore) is the primary APAC node, with