What is included in the Instance price?

Our GPU Instances' prices include the vCPU and RAM needed for optimal performance. To launch the L4 GPU Instance you will need to provision a minimum of Block Storage and a flexible IP at your expense. Any doubt about the price, use the calculator, it's made for it!

What are the differences between L4-1-24G, L4-2-24G, L4-4-24G and L4-8-24G?

These are 4 formats of the same instance embedding NVIDIA L4 Tensor Core GPU. L4-1-24G embeds 1 NVIDIA L4 Tensor Core GPU, offering a GPU memory of 24GB L4-2-24G embeds 2 NVIDIA L4 Tensor Core GPUs, offering a GPU memory of 2 times 24GB. L4-4-24G embeds 4 NVIDIA L4 Tensor Core GPUs, offering a GPU memory of 4 times 24GB. L4-8-24G embeds 8 NVIDIA L4 Tensor Core GPUs, offering a GPU memory of 8 times 24GB.

Can I use MIG to get the most out of my GPU?

NVIDIA Multi-Instance GPU (MIG) is a technology introduced by NVIDIA to enhance the utilization and flexibility of their data center GPUs, specifically designed for virtualization and multi-tenant environments. This features is available on H100 PCIe GPU Instance but not on the L4 GPU Instance. However users can benefit from Kubernetes Kapsule compatibility to optimize their infrastructure. Learn more

How to choose the right GPU for my workload?

There are many criteria to take into account to choose the right GPU instance: Workload requirements Performance requirements GPU type GPU memory CPU and RAM GPU driver and software compatibility Scaling For more guidance read the dedicated documentation on that topic

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L4 GPU Instance

Maximize your AI infrastructure's potential with a versatile and cost-effective GPU Instance

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Versatile usage

Leveraging efficient and fast image and video decoding, rendering, as well as Deep Learning model training and inference, the L4 GPU Instance covers a versatile range of needs.

Affordability without compromise

Offering a cost-effective alternative to higher-priced GPUs, the L4 GPU Instance achieves solid performance without burdening restricted budgets, enabling startups and small-scale projects.

AI Video Excellence

L4 GPU Instance delivers excellent video generation, decoding, and pre-post processing, empowering industries like fashion, architecture, gaming, and advertising with coherent visual content creation.

Available zones:

Paris:PAR 2

Warsaw:WAW 2

L4 GPU technical specifications

GPUNVIDIA L4 Tensor Core GPU

GPU Memory24GB GDDR6 (300 GB/s)

Processor8 vCPUs AMD EPYC 7413

Processor frequency2.65 Ghz

Memory48 GB of RAM

Memory typeDDR4

Network Bandwidth2.5 Gbps

StorageBlock Storage

CoresTensor Cores 4th generation RT Cores 3rd generation

Ideal use cases with the L4 GPU Instance

Accelerate Image and video generation affordably

If you’ve put a text-to-image model in production, and you’re looking to optimize the cost of you’re infrastructure but not at the cost of performance. L4 Instance GPU is a serious candidate.

L4 GPU Instance generates a 256x256px image in 14445.1 pixel/second

It’s 56% faster than with a Render Instance (9278.3 pixel/second)
It’s 6,3% faster than with a T4 GPU Instance (13583.3 pixel/second)
It’s 8.5% faster than with a V100 PCIe GPU (16G) Instance (13314.6 pixel/second)
It’s 8,2% faster than with a V100 SXM2 GPU (16G) Instance (13348.8 pixel/second)
And almost as fast as A100 SXM 40GB (-1,6%) (14681.1pixel/second)

And with 50 percent more memory capacity, L4 enables larger image generation, up to 1024x768, which wasn’t possible on the previous GPU generation (T4)

Source: Model tested InvokeAI a popular open-source framework for image generation and modifications. On top of it Cloud Mercato created invokeai-benchmark, a handy tool make our tests and methodology more easily reproducible.

Build and monitor a flexible and secured cloud infrastructure powered by GPU

Benefit from a complete cloud ecosystem

Cockpit

Monitor your infrastructure and applications in real-time with a fully managed observability solution.

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Kubernetes Kapsule

Match any growth of resource needs effortlessly with an easy-to-use managed Kubernetes compatible with a dedicated control plane for high-performance container management.

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