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T4 on Google Cloud: Pricing, Specs & How to Rent

Deploybase · April 22, 2025 · GPU Pricing

Contents

Google Cloud T4 Pricing

Google Cloud T4 runs $0.35-0.45/hour on-demand. Regional pricing varies, but typically tracks other major clouds for entry-level GPUs.

Lock in a one-year commitment and save 30%. Three-year deals hit 50% off. That brings effective cost to $0.17-0.23/hour.

Preemptibles are cheapest-25-30% of on-demand. The tradeoff: Google terminates them without warning after ~24 hours. Batch jobs handle this fine. Production services don't.

Storage costs add significantly for persistent data. Persistent disks cost $0.10-0.20 per GB-month. Large datasets quickly accumulate storage expenses. Google Cloud Storage provides cheaper long-term storage, though access speeds are slower than locally attached disks. Data transfer costs apply when moving datasets between storage services.

T4 GPU Specifications

The NVIDIA T4 GPU delivers solid performance for inference and lighter training workloads. It features 16GB of GDDR6 memory, supporting batch inference for moderate-sized models. The T4 provides 8.1 teraflops of FP32 throughput and 130 TOPS of INT8 performance.

Memory bandwidth reaches 320 GB/s, adequate for most natural language processing and computer vision inference tasks. The T4 excels at inference work where throughput matters more than latency. Batch sizes can reach hundreds for models fitting in 16GB memory. Lower latency requirements benefit from smaller GPU configurations.

The T4 supports INT8 quantization, reducing model sizes and memory requirements. Quantized models can serve larger batches or handle longer sequences. Inference libraries like TensorRT and ONNX Runtime provide optimizations specific to T4 hardware, improving throughput further.

Power consumption remains low at 70W, allowing efficient deployment in edge environments and smaller data centers. Google's infrastructure handles cooling and power distribution smoothly. Cost-conscious teams appreciate the low-power envelope reducing data center expenses.

The T4 lacks advanced features like sparsity acceleration and advanced mixed-precision support found in newer GPUs. Practitioners should compare performance against larger GPUs when throughput requirements are unclear. Benchmarking on actual workloads reveals if T4s suffice or if investment in newer hardware is necessary.

How to Rent T4 on Google Cloud

Go to the Google Cloud Console, pick the region and zone (balance cost vs latency), then select how many T4s (1-8 per instance).

Choose the OS image. Google has pre-baked PyTorch and TensorFlow images, or bring custom ones. Boot disk size matters for temp files and startup speed.

Set up SSH keys and IAM permissions. Nothing fancy-standard security practice. gcloud CLI handles most of this from the local machine.

GCP Storage integrates directly (no egress fees for internal transfers). Use Cloud Monitoring to watch GPU utilization and catch bottlenecks.

Google Cloud vs Other Platforms

Google Cloud's T4 pricing competes effectively with AWS and Azure equivalents. On-demand rates are comparable, though commitment discounts vary. Google's preemptible instances offer the deepest discounts, beneficial for batch workloads. AWS spot instances provide similar functionality at slightly different pricing.

Integration with Google's machine learning ecosystem provides advantages for GCP users. Vertex AI automates common ML tasks and integrates natively with Compute Engine. TensorFlow development often benefits from optimization for Google hardware. Teams heavily invested in Google services find migration costs reduced.

BigQuery integration allows direct data access for training pipelines without intermediate transfers. This tight coupling reduces data movement costs and complexity. Other cloud providers require more manual orchestration for equivalent workflows.

Support quality matches production standards across all major providers. Google Cloud offers technical support through various tiers, from community forums to 24/7 phone support. Response times and expertise vary with support tier selection.

Pricing transparency and cost management tools favor Google Cloud. The cost estimator accurately predicts expenses before committing to configurations. Detailed billing reports show per-service spending, aiding in cost optimization. Other providers offer similar tools but Google's interfaces are particularly user-friendly.

Review Google Cloud GPU pricing for comprehensive comparison. Check T4 specifications for hardware details. Explore GPU pricing guide for broader market analysis.

FAQ

Q: What's the minimum commitment period for Google Cloud T4s?

A: On-demand instances have no minimum. Users pay hourly and can terminate at any time. Commitment discounts require one-year or three-year commitments but offer substantial savings for planned workloads. Preemptible instances charge per-minute and can terminate suddenly without advance notice.

Q: Can I use Google Cloud's T4s for real-time inference?

A: Yes. T4s serve inference workloads well, particularly for moderate-throughput scenarios. Latency-sensitive applications may require larger GPUs or GPU acceleration outside the critical path. A100 and H100 instances serve higher-throughput inference, though at greater cost.

Q: Does Google Cloud offer auto-scaling with T4 GPUs?

A: Yes. Instance groups support automatic scaling based on utilization metrics. Workload-based metrics can trigger instance creation and deletion. Cloud Run and AI Platform offer serverless GPU options with automatic scaling, though with less control over underlying hardware.

Q: How much does data transfer out of Google Cloud cost?

A: Egress to the internet costs $0.12 per GB. Internal transfers between Google services incur no charge. Teams moving large datasets in and out of Google Cloud should factor transfer costs. Persistent local storage is preferable when possible to avoid egress charges.

Q: Can I use Google Cloud T4s with TensorFlow directly?

A: Yes. Google's deep learning VMs come with TensorFlow pre-installed and optimized for Google hardware. Google Cloud provides extensive TensorFlow documentation and tutorials. Vertex AI simplifies TensorFlow training at scale.

Understanding machine learning infrastructure economics helps select appropriate platforms and resources. Pricing comparison across regions and commitment levels identifies cost optimization opportunities. Performance benchmarking guides GPU selection for specific workloads.

Review Google Cloud GPU pricing for complete pricing details. Check GPU pricing guide for provider comparison. Study fine-tuning guide to understand common T4 use cases.

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