About Zillion Network

This startup offers a platform for sourcing, deploying, and managing GPU clusters, streamlining hardware deployment and system provisioning for businesses. Their platform specializes in designing and operating large-scale GPU clusters, simplifying troubleshooting and asset management.

```xml <problem> Building and managing large-scale GPU clusters for AI and HPC workloads is complex and time-consuming, often resulting in delayed deployments and increased failure rates. Sourcing the necessary hardware, setting up high-speed networking, and configuring parallel frameworks across multiple nodes adds significant overhead. Troubleshooting and maintaining peak performance further strains resources and expertise. </problem> <solution> Zillion Network provides a platform for rapidly sourcing, deploying, and managing GPU infrastructure, offering bare metal, virtual machines, containers, and notebook environments. The platform streamlines hardware deployment and system provisioning, automating infrastructure as code and providing centralized management for clusters of 10,000+ servers. Zillion Network simplifies the deployment of AI infrastructure by offering pre-built containers and Helm charts for NVIDIA Inference Microservices, along with industry-standard APIs and optimized inference engines. Their advanced Site Reliability Engineering (SRE) approach reduces failure rates through continuous monitoring, troubleshooting, and optimization of GPU utilization. </solution> <features> - Rapid deployment of GPU resources, including bare metal servers, virtual machines, containers, and Jupyter notebooks - Support for a range of NVIDIA GPUs, including HGX H100, L40S, RTX 6000 Ada, and RTX 4090 - Automated infrastructure-as-code deployment for scalable and reproducible environments - Centralized management of large-scale GPU clusters with 10K+ servers - Pre-built containers and Helm charts for NVIDIA Inference Microservices (NIM) - Integration with high-speed networking solutions like InfiniBand and RoCEv2 - Continuous monitoring and troubleshooting to minimize downtime and maintain peak performance - Optimization of GPU utilization through workload balancing - Kubernetes cluster deployment and management at scale </features> <target_audience> The primary target audience includes organizations and researchers involved in AI, machine learning, and high-performance computing who require readily available and expertly managed GPU resources. </target_audience> ```

What does Zillion Network do?

Employees

2 employees

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Zillion Network

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Executive Summary

zillion.network 10+

Funding

No funding information available.

Team (<5)

No team information available.

Company Description

Problem

Building and managing large-scale GPU clusters for AI and HPC workloads is complex and time-consuming, often resulting in delayed deployments and increased failure rates. Sourcing the necessary hardware, setting up high-speed networking, and configuring parallel frameworks across multiple nodes adds significant overhead. Troubleshooting and maintaining peak performance further strains resources and expertise.

Solution

Zillion Network provides a platform for rapidly sourcing, deploying, and managing GPU infrastructure, offering bare metal, virtual machines, containers, and notebook environments. The platform streamlines hardware deployment and system provisioning, automating infrastructure as code and providing centralized management for clusters of 10,000+ servers. Zillion Network simplifies the deployment of AI infrastructure by offering pre-built containers and Helm charts for NVIDIA Inference Microservices, along with industry-standard APIs and optimized inference engines. Their advanced Site Reliability Engineering (SRE) approach reduces failure rates through continuous monitoring, troubleshooting, and optimization of GPU utilization.

Features

Rapid deployment of GPU resources, including bare metal servers, virtual machines, containers, and Jupyter notebooks

Support for a range of NVIDIA GPUs, including HGX H100, L40S, RTX 6000 Ada, and RTX 4090

Automated infrastructure-as-code deployment for scalable and reproducible environments

Centralized management of large-scale GPU clusters with 10K+ servers

Pre-built containers and Helm charts for NVIDIA Inference Microservices (NIM)

Integration with high-speed networking solutions like InfiniBand and RoCEv2

Continuous monitoring and troubleshooting to minimize downtime and maintain peak performance

Optimization of GPU utilization through workload balancing

Kubernetes cluster deployment and management at scale

Target Audience

The primary target audience includes organizations and researchers involved in AI, machine learning, and high-performance computing who require readily available and expertly managed GPU resources.