Sustained use
For longer training cycles or projects that need stable resources.
AI COMPUTE
AI compute rental, planned for your project.
AROXEON evaluates dedicated GPU nodes or multi-node clusters around the model, memory, interconnects, storage, software, and duration.
01
When dedicated compute makes sense
Dedicated nodes or clusters fit projects that need stable resources, a separate environment, or sustained use.
Defined software environment
For specific frameworks, drivers, containers, or data paths.
Team collaboration
Plan resource allocation, access, and result handling.
Defined delivery
Confirm configuration, deployment, acceptance, and support in advance.
Single node or multi-node depends on the task
A single node centers on its resource mix. Multi-node work also needs parallelism, communication, and shared data.
Single node
Evaluate GPU memory, card count, intra-node fabric, CPU and RAM, local data, and software.
Multi-node
Add communication, cluster networking, shared storage, scheduling, and operating support.
Final configuration, supply, and commercial terms are confirmed per project.
Tell us these details
You can start a conversation without a complete technical document.
Model and task
Model family or scale, and whether the work is training, fine-tuning, or inference.
Memory and parallelism
Known memory needs, precision, batch size, and any existing parallel strategy.
Data path
Dataset size and access, model files, checkpoint frequency, and archive needs.
Software environment
Operating system, framework, container, driver, or existing constraints.
Duration
Expected start, continuous or phased use, and number of collaborators.
Access and boundaries
Access method, service network, permissions, acceptance, and support ownership.
Different tasks need different configurations
Training
Evaluate compute density, GPU and node communication, data throughput, and sustained runtime.
Fine-tuning
Evaluate model scale, method, precision, data volume, and experiment parallelism.
Inference
Evaluate latency, throughput, concurrency, model loading, and service integration.
Confirm a compute plan in four steps
Timing and delivery depend on the project.
01 · Describe the task
Share the goal, model, data, and expected duration.
02 · Identify constraints
Confirm memory, network, storage, and software needs.
03 · Evaluate combinations
Shape a node or cluster plan and identify validation needs.
04 · Confirm boundaries
Agree on configuration, commercials, deployment, acceptance, and support.
Compute rental · Project evaluation
Compute configuration and supply are confirmed per project
Share your workload and schedule. We will discuss available configurations, pricing, and delivery terms based on actual supply.
Discuss a projectCompute FAQ
01Why are GPU models and prices not listed?
GPU models, inventory, and prices change with actual supply, so available configurations and commercial terms are shared during project discussions.
02Can I ask about a single server?
Yes. Single-node, multi-node, and other configurations all start with the actual workload.
03Is there a fixed SLA?
Support scope, reliability targets, and acceptance criteria are confirmed in the project plan.
04Do I need a full technical document first?
No. A model or task type, data scale, duration, and current environment are the most useful starting points.
PROJECT ENQUIRY
Tell us about your compute or infrastructure needs.
Email us about compute rental, infrastructure, Libra, or a business partnership.