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Google Cloud Run supports AI inference on NVIDIA L4 and NVIDIA RTX PRO 6000 Blackwell GPUs, attached to containerized services as managed capacity. Each instance gets one GPU, and the service can scale to zero. GPU workloads require instance-based billing, however, and GPU time is billed for the full instance lifecycle—not only while a request is being processed. Current configuration details and supported regions are documented in Google Cloud’s Cloud Run GPU documentation.
What Cloud Run GPU provides
Cloud Run adds a supported NVIDIA GPU to a containerized service instance. Google manages the GPU drivers, and its documentation describes GPU capacity as available on demand without reservations. GPU-backed services can run LLM inference and other compute-intensive tasks such as video transcoding and 3D rendering.
The service documentation lists two GPU options. Their VRAM is separate from the instance’s system memory:
| GPU | VRAM | Minimum service configuration | Documented regions |
|---|---|---|---|
| NVIDIA L4 | 24 GB | 4 CPU and 16 GiB memory | asia-southeast1, asia-south1 (invitation only), europe-west1, europe-west4, us-central1, us-east4 |
| NVIDIA RTX PRO 6000 Blackwell | 96 GB | 20 CPU and 80 GiB memory | asia-southeast1, asia-south2, europe-west4, us-central1 |
These configuration and region details come from Google’s current service documentation, accessed in 2026; check the live page for updates and project-specific capacity or quota caveats. Both GPU types are documented with NVIDIA driver version 580.x.x (13.0). One GPU is available per instance. In a sidecar setup, only one container can have the GPU attached.
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Google says a supported GPU instance with its preinstalled drivers can start in approximately five seconds, meaning the container processes can use the GPU. That figure is not an end-to-end model-serving cold-start time: model download or loading and inference can add time.
Choose a GPU based on model fit and deployment constraints
NVIDIA L4
The L4 has 24 GB of documented VRAM and a lower minimum CPU and memory configuration than the RTX PRO 6000 Blackwell. Its listed regions include four locations also listed for Blackwell—asia-southeast1, europe-west4, and us-central1—plus europe-west1, us-east4, and invitation-only asia-south1. Whether it suits a model depends on that model’s memory needs and your project’s available quota and capacity.
NVIDIA RTX PRO 6000 Blackwell
Blackwell has 96 GB of documented VRAM and requires at least 20 CPU and 80 GiB of instance memory. Its documented region list is shorter than L4’s, so confirm location support before selecting it. More VRAM may accommodate workloads that do not fit within L4’s documented VRAM, but these specifications alone do not establish a universal performance or cost advantage.
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Compare the options using your model’s memory requirements, the minimum instance configuration, supported regions, project quota and current pricing. Google’s documentation does not establish a universal price or performance winner.
Check region, quota and capacity before deploying
GPU region support and quota are not interchangeable with guaranteed physical capacity. Google’s documentation says initial quota is granted by region on first deployment using the documented non-zonal-redundancy configuration: up to three L4 GPUs, or the equivalent of three RTX PRO 6000 Blackwell GPUs (3,000 milliGPUs). Larger needs require a quota increase. The stated initial quota does not guarantee that capacity will be available under every demand condition.
- Check the current GPU region list against the region you plan to use; the two GPU types have different lists.
- Check quota in the project and region where the service will run. Some regions have additional capacity or quota caveats.
- For a deployment larger than the initial documented allowance, request a quota increase and confirm capacity before relying on it.
Understand billing and zonal redundancy
GPU services must use instance-based billing. Google bills GPU time for the full instance lifecycle, and minimum instances are charged at the full rate while idle. The GPU feature has no per-request GPU fee. Scaling to zero can avoid keeping an instance running between periods of demand, but it does not change the lifecycle billing rule for an instance that is running.
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Cloud Run’s documented default enables GPU zonal redundancy. The setting changes the balance between GPU availability during zonal disruption and GPU-second cost:
| Setting | Capacity and failover | GPU-second cost |
|---|---|---|
| Zonal redundancy enabled | Capacity is reserved across multiple zones to improve the chance of handling traffic shifted after a zonal outage. | Higher than with redundancy disabled. |
| Zonal redundancy disabled | Failover is best-effort and depends on unused GPU capacity being available. | Lower than with redundancy enabled. |
The applicable service SLA depends on the redundancy configuration. Google’s current documentation should be consulted for the applicable terms and current prices; no dollar amount is stated here.
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Google’s Gemma 4 and vLLM Cloud Run codelab, updated May 7, 2026, demonstrates serving Gemma 4 E2B with an RTX PRO 6000 Blackwell GPU. Its example configures 20 CPU, 80 GiB of memory, one GPU, disabled GPU zonal redundancy, a service account, no unauthenticated access, and a startup probe. It enables the Cloud Run, Cloud Build and Artifact Registry APIs.
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- Confirm prerequisites. Choose a supported region, verify quota and capacity for the project, and check that the selected GPU’s documented minimum resources fit the service.
- Enable the required APIs. Follow the codelab’s steps to enable Cloud Run, Cloud Build and Artifact Registry for the project.
- Configure the service and container. Use the codelab’s vLLM and Gemma 4 E2B example as a starting point, including its service account, authentication setting and startup probe.
- Review the GPU and reliability settings. The example uses one RTX PRO 6000 Blackwell GPU and disables zonal redundancy. Choose that setting only if best-effort GPU failover is acceptable for your service.
- Validate the example against your workload. Before deployment, confirm current image tags and flags, region support, model requirements and project quota. A tutorial configuration is not a guarantee of performance or availability for another model or production workload.
What Google’s launch announcements do—and do not—show
Google’s August 21, 2024 announcement described the NVIDIA L4 GPU as a preview feature. Later, Google announced general availability and reported approximately 19 seconds to first token for a specific Gemma 3 4B example, including startup, model loading and inference. That is a vendor-reported result for that workload, not a general latency guarantee or an independent benchmark. Current service documentation now lists both L4 and RTX PRO 6000 Blackwell; use it for present-day configuration and region details.
The 2024 announcement quoted Anne Hecht, then NVIDIA’s Senior Director of Product Marketing, saying: “With the addition of NVIDIA L4 Tensor GPU and NVIDIA NIM support, Cloud Run provides users a real-time, fast-scaling AI inference platform to help customers accelerate their AI projects and get their solutions to market faster — with minimal infrastructure management overhead.” This is a vendor statement in Google Cloud’s announcement, not an independent performance assessment.
Google’s general-availability announcement also quoted Dave Salvator, director of accelerated computing products at NVIDIA: “Serverless GPU acceleration represents a major advancement in making cutting-edge AI computing more accessible. With seamless access to NVIDIA L4 GPUs, developers can now bring AI applications to production faster and more cost-effectively than ever before.” Treat that as an attributed vendor quotation, not evidence that Cloud Run is the least expensive option for every workload.
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