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Build your own GPU Cloud
USE CASE
Fully integrated, multi-tenant, multi-vendor server, switch and storage management for GenAI, GPU intensive analytics and big data.
Success stories
Discover how an AdTech company reduced therir costs by moving to on-premises.
Read the case study
Discover how HEY was moved
to on-premises to save millions.
Read the 3rd party case study
Transformer Models:
Unleashing Next-Gen Use Cases
The Transformer model architecture has propelled the use of Large Language Models (LLMs) enabling numerous use cases like text summarization, Chatbots, translation, Code assist, entity extraction.
The growing adoption of machine learning and AI across various industries has increased the demand for robust computing resources
As an IT administrator it can become very challenging to meet requirements from the data science teams, whether that’s an individual data scientist, a data engineer, a platform engineer, getting the GPU card installed and getting the drivers deployed in a fashion where they can be utilized by the overlying application can take days, weeks, even months to provision.
Challenge
MetalSoft enables clients to build GPU Clouds, that allows end users to have a cloud like experience where they can consume GPU infrastructure in matter of minutes. Our platform enables businesses to access high-performance computing resources, such as graphics processing units (GPUs), on-demand, without the burden of managing complex hardware and network setups.
Solution
Riding the Wave: Seizing The Explosive Potential of GPU-as-a-Service
In 2023 the GPU-as-a-Service market size was valued at USD 3.16 billion. It is projected to grow to 25.53 billion by 2030, exhibiting a CAGR of 34.8% during the forecast period
MetalSoft software platform enables fully automated GPU-as-a-Service infrastructure
MetalSoft platforms support unique requirements depending on your use case, whether it’s for hyper-parameter search, large-scale distributed training, or production inference.
Engage us for your GPU Cloud project, where we assist with cluster design, rack design, node design, cluster orchestration, resource allocation, and container architecture. We also help build the right storage architecture for you, whether it’s single-tier, parallel cluster, or tiered storage. For networking, we support both single-switch topology and leaf-spine non-blocking topology.
Tailored Solutions for Your GPU Cloud Project
Take the next step
Get in touch with us to request a demo or to talk to our technical sales team.
70%
of data center outages can be attributed to human error¹.
71%
of organizations will implement infrastructure automation by 2025².
77%
of respondents cite talent shortages as a top barrier to digital transformation³.
Riding the Wave: Seizing The Explosive Potential of GPU-as-a-Service
In 2023 the GPU-as-a-Service market size was valued at USD 3.16 billion. It is projected to grow to 25.53 billion by 2030, exhibiting a CAGR of 34.8% during the forecast period
Single API for entire stack
RESTful APIs
Customizeable Workflow management
Infrastrcuture Services
Job queue
OPAM
DNS
DHCP
RBAC
Meter
Infrastrcuture Services
Template based install of OS/Hypervisor/K8x
High-speed fabric for Training & Inferencing
Node discovery & Provisioning
Storage provisioning for ML model & datasets
Networking
EVPN-VxLAN
OOB
PCIe
SAN
BGP
MCLAG
Compute
CPU
GPU
BIOS
Disks
RAM
RDMA
Storage
SSD
NVME
iCSI
FC
NFS
HDD
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