GPU Compute
Arxhe Inference Service

Faster spin-up times. More responsive autoscaling.

Serve better inference and autoscale across thousands of GPUs as demand changes—so you never get crushed by user growth.

Talk to our team

Serve inference faster with a solution
that scales with you.

 Arxhe Inference Service offers a modern way to run inference that delivers better performance and minimal latency while being more cost-effective than other platforms.

See what makes our solution different:

Traditional tech stack

Managed cloud service

Most cloud providers built their architecture for generic use cases and hosting environments rather than compute-intensive use cases.

  • VMs host Kubernetes (K8s), which need to run through a hypervisor
  • Difficult to scale
  • Can take 5-10 min. or more to spin up instances

Arxhe’s tech stack

Multi-modal or serverless Kubernetes in the cloud

Deploy containerized workloads via Kubernetes for increased portability, less complexity, and overall lower costs.

  • No hypervisor layer, so K8s runs directly on bare metal (hardware)
  • We leverage Kubevirt to host VMs inside K8s containers
  • Easy to scale
  • Spin up new instances in seconds
  • Autoscaling

    Optimize GPU resources for greater efficiency and less costs.

    Autoscale containers based on demand to quickly fulfill user requests significantly faster than depending on scaling of hypervisor backed instances of other cloud providers. As soon as a new request comes in, requests can be served as quickly as:

    • 5 seconds for small models
    • 10 seconds for GPT-J
    • 15 seconds for GPT-NeoX
    • 30-60 seconds for larger models
    Inference Service
  • Model Training
    Serverless Kubernetes

    Deploy models without having to worry about correctly configuring the underlying framework. 

    KServe enables serverless inferencing on Kubernetes on an easy-to-use interface for common ML frameworks like TensorFlow, XGBoost, scikit-learn, PyTorch, and ONNX to solve production model serving use cases. 

  • Networking

    Get ultramodern, high-performance networking out-of-the-box.

    Arxhe's Kubernetes-native network design moves functionality into the network fabric, so you get the function, speed, and security you need without having to manage IPs and VLANs.

    • Deploy Load Balancer services with ease
    • Access the public internet via multiple global Tier 1 providers at up to 100Gbps per node
    • Get custom configuration with Arxhe Virtual Private Cloud (VPC)
    Direct Kubernetes Access
  • Model Training
    Storage

    Easily access and scale storage capacity with solutions designed for your workloads.

    Arxhe Cloud Storage Volumes are built on top of Ceph, an open-source software built to support scalability for enterprises. Our storage solutions allow for easy serving of machine learning models, sourced from a range of storage backends, including S3 compatible object storage, HTTP and a Arxhe Storage Volume.

Save costs on inference from top to bottom.

From optimized GPU usage and autoscaling to sensible resource pricing, we designed our solutions to be cost-effective for your workloads. Plus, you have the flexibility to configure your instances based on your deployment requirements.

  •  Bare-metal speed and performance

    We run Kubernetes directly on bare metal, giving you less overhead and greater speed.

  • Scale without breaking the bank

    Spin-up 1,000s of GPUs in seconds and scale to zero during idle time, consuming neither resources nor incurring billing.

  • No fees for ingress, egress, or API calls

    Pay only for the resources you use and choose the solutions that enable you to run as cost effectively as possible.

Case Study: Tarteel AI

From limited GPU options to blazing fast
inference speeds.

Learn how Tarteel AI leveraged Zeet to smoothly move its deployment from AWS to Arxhe, translating to a 22% improvement in latency and ~56% cost reduction.

Talk to our experts today.

Serving inference is critical to the success of your application. Learn how Arxhe can help you get started with compute resources that fit your workloads.

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