Maximized GPU utilization
10%
80%
“By unifying scattered GPUs, we raised utilization from 10% to over 80% and maximized the value of existing resources with no extra GPU investment.”
Leading global manufacturer A
Don't buy more GPUs.
Run 8x more AI workloads
with the GPUs you already have.
Server prices have doubled. Lead times are over a year.
And yet over 80% of your GPUs sit idle.
AIPub Enterprise runs on the GPUs you already have. Start your AI projects right away — no new hardware required.
AI infrastructure cost
isn't just about GPU prices.
You buy more than you need, underutilize it, and operations keep getting more complex.
TEN cuts infrastructure waste to lower TCO and raise the value of your resources.
Overprovisioned “just in case” infrastructure ends up as wasted spend. We size your resources to your actual AI workloads, so you stop overinvesting.
- Workload analysis
- Optimal design
- CAPEX savings
GPUs locked to a single team or workload go underused. We partition GPUs and allocate them in real time, right where they are needed, minimizing idle capacity.
- GPU sharing
- Max utilization
As teams, projects, and GPUs grow, so does operational complexity. We automate and unify operations so overhead and cost stay low even as you scale.
- Automation
- Unified management
- Lower ops cost
Beyond GPU purchases — power, cooling, cloud, and ops staff — hidden costs keep piling up in AI infrastructure. We surface resource usage and cost to find waste and steadily lower your TCO.
- FinOps
- TCO optimization
The TEN technology stack
that lowers AI infrastructure TCO
From build to operations, RA:X and AIPub optimize
your AI infrastructure cost and resource efficiency.
Just tell us what you’re building and why.
TEN benchmarks your actual AI workloads to size the required GPU performance and resources, then proposes the optimal infrastructure setup.
Prevent over-investment
Shared, but performance is not.
AIPub uses spatial partitioning — not time-slicing — to allocate GPU memory and cores independently per workload. Multiple workloads share one GPU at once with minimal interference, delivering over 8x better workload isolation than time-slicing.
Higher GPU utilization
The GPUs you need, the moment you need them.
With TEN’s first-of-its-kind spatial-partitioning dynamic allocation, GPUs are distributed in real time to match workload demand. Spare resources are automatically reclaimed and reallocated, so every GPU is fully used.
Minimize idle resources
Limited GPUs? Critical work first.
TEN’s in-house scheduler lets you set per-workload priorities and control resource allocation. Critical work gets GPUs first, so limited resources run more efficiently aligned with your organization’s priorities.
Optimized allocation, stronger operational control
Not just monitoring — optimizing power too.
Track GPU and cluster health in real time with 100+ monitoring metrics, and control power down to a single GPU. Cut unnecessary power draw based on resource state to lower AI infrastructure operating costs.
Better operational visibility, lower power costs
Know exactly who’s using what.
Measure and manage GPU usage per user, team, and project to see clearly where and how much is spent. Manage costs based on real usage data and optimize allocation and infrastructure investment decisions.
Cost visibility, continuous TCO optimization
How do leading companies run their AI infrastructure?
See how real teams maximized GPU utilization and scaled AI faster with AIPub.
Unified GPU infrastructure at scale
60+
“We unified 60+ nodes scattered across teams to raise utilization and cut the operational burden and TCO of large-scale infrastructure.”
Public research institute
Multitenancy & cost optimization
Reduced idle resources
“We built a system for many users and research teams to share GPUs efficiently and manage them by usage, reducing idle resources and unnecessary investment.”
Leading Korean university
Unified AI inference
Lower management costs
“Running dozens of ML- and LLM-based AI services reliably in one environment cut infrastructure complexity and management cost.”
Leading global manufacturer B
Unified distributed environments
Reduced operational burden
“Unifying GPU clusters from different dev and ops environments into one system reduced management burden and raised operational efficiency.”
Leading global manufacturer C
Leveraging existing investment
Lower build costs
“Extending existing VM-based GPU infrastructure into a container-based AI environment cut new build cost and got more from our existing investment.”
State-owned enterprise D
From NVIDIA to Cisco —
a platform trusted by global partners
Choose the product that fits your scale and goals
Not sure which one is right for you?
Contact us
RA:X Consulting
Workload-based AI infrastructure sizing and optimization consulting
AIPub Enterprise
Enterprise AI infrastructure operations and orchestration platform
Coming soon
AIPub Cloud
Managed AI Cloud service unifying GPU infrastructure and operations
Modular Data Center
A modular AI data center deployed fast at the scale and location you need
Before you buy more servers,
get an assessment first.
Are your GPUs actually being used?
Could you run more projects without buying more?
RA:X benchmarking answers both — with data.
