GPU & memory monitoring
Live usage and trends per host.
GPU & parallel computeBeta
GPU power, under your control
Prepare and monitor your organization’s GPU servers for inference and parallel workloads; models and data never leave your network.
- NVIDIA
- driver support
- Ollama
- local inference
- Live
- GPU utilization
Live
HOW IT WORKS
How it works
- 1
Connect a GPU server
Connect it like any other server with one command.
- 2
Hardware discovery
The agent reports GPU model, memory and driver.
- 3
Run workloads
Deploy an AI model or your own workload on the GPU.
- 4
Monitor
See utilization, temperature and memory per GPU.
Capabilities
GPU & parallel compute
Local models
Install Ollama and open models in one click.
Isolated containers
Workloads in constrained containers.
Capacity alerts
Memory pressure and thermal warnings.
Controlled access
Per-agent install permission.
Run history
Every install and analysis is recorded.
USE CASES
Use cases
LLM inference
Run open models with low latency on the internal network.
Training and fine-tuning
Make the most of the GPUs you already own.
Shared across teams
Allocate GPUs to projects with roles and audit.
Technical specs
- Hardware
- NVIDIA with official drivers
- Runtime
- Docker + NVIDIA Container Toolkit
- Monitoring
- Utilization, memory, temperature
- Status
- Beta
LOCAL AI
AI without data egress
Models run on your GPUs and only results appear in the console.
- Chat with the local model in the console
- Infrastructure analysis with admin approval
- Nothing sent to external services
FAQ
Frequently asked questions
Do we rent GPUs from whycloud?
No; you connect your own GPUs.
Which models are supported?
Open models compatible with Ollama such as Qwen and Gemma.
Is AMD supported?
The focus is NVIDIA today; other hardware is on the roadmap.
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