Faster spin-up times. More responsive autoscaling.
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
Our US-based Render Wranglers are available to troubleshoot issues, ensuring that your projects stay on track. We render hundreds of projects every day, and have seen it all. Yeehaw.
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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

· 2.10 seconds for GPT-J

· 3.15 seconds for GPT-NeoX

· 4.30-60 seconds for larger models

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.

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

MegaSpeed.Ai'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 MegaSpeed.Ai Virtual Private Cloud (VPC)

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