Focus on your models while we run the infrastructure
Managed Kubernetes, Ray, MLflow and vLLM or SGLang endpoints on dedicated GPUs we run for you. Your ML engineers train and serve models without needing DevOps expertise.
From experiment to endpoint
One path from a GPU allocation to a model in production
Allocate
Give a team GPUs, whole or as MIG slices, with drivers already installed.
Train
Run distributed jobs with Ray on Kubernetes, tracked in MLflow as they go.
Register
Promote the best run to the model registry with its data and parameters attached.
Serve
Deploy it on vLLM or SGLang behind an alias your applications already call.
Capabilities
Train, track and serve on dedicated GPUs
Use what you need today and add the rest as your work grows. Further capabilities are available on request.
Compute management
- GPU allocation
- Assign GPUs and MIG slices to your organisation and the teams inside it.
- Drivers built in
- CUDA and drivers come installed, so work can start straight away.
- Deployment templates
- Start from tested templates for common training and serving setups.
Training and MLOps
- Ray on Kubernetes
- Run training jobs across the GPUs reserved for your firm, from a notebook, script or CI.
- MLflow tracking and registry
- Every run is tracked, and the models worth keeping are registered.
- Lifecycle management
- Move models from experiment to production and retire them cleanly.
Inference
- vLLM and SGLang
- Serve catalogue models or your own weights on the engine that suits them.
- Model endpoints
- Train a model and host it yourself, callable through the PrivateMind API.
- Model aliasing
- Point stable names at models, so upgrades never need a code change.
Storage and security
- Volumes and buckets
- Provision storage for data and weights, available to GPUs and CPUs.
- Vaults and audit logging
- Keep secrets in managed vaults and record who did what.
- GitOps deployments
- Describe deployments in Git and let the platform apply them.
Fine-tuning
A LoRA or QLoRA fine-tune is a single function call
The Python SDK checks your configuration and dataset before a GPU is allocated, so problems surface in seconds. The finished adapter lands on your volume and the run is recorded in MLflow.
- Submit from anywhere. Notebooks, scripts and CI all use the same interface.
- Dry runs. Preview the resolved settings before anything is submitted.
- Serve the result. Deploy the adapter as an endpoint on the same GPUs.
import privatemind as pm
run = pm.finetune(
model="Qwen/Qwen3-8B-Instruct",
dataset="sft_data.jsonl",
output_path="/workspace/adapters/support-v1",
method="qlora",
)
run.wait(progress=True)
print(run.mlflow_run_id)Questions
Control Plane questions
Something not covered here? Talk to our team or read the training docs.
What is the PrivateMind Control Plane?
An MLOps platform on dedicated GPUs we run for you. Your ML engineers allocate GPUs, run distributed training with Ray on Kubernetes, track every run in MLflow and serve models on vLLM or SGLang endpoints, without needing DevOps expertise.
Can we train and serve our own models?
Yes. Run training jobs across the GPUs reserved for your firm, promote the best run to the MLflow model registry, then deploy it on vLLM or SGLang behind an endpoint callable through the PrivateMind API.
Can we fine-tune a model?
A LoRA or QLoRA fine-tune is a single function call in the Python SDK. It checks your configuration and dataset before a GPU is allocated, the finished adapter lands on your volume, and the run is recorded in MLflow.
What happens when a model we serve needs an upgrade?
Model aliasing points stable names at models, so upgrades never need a code change. Point the alias at the new model and your applications keep calling it.
Train and serve your own models privately
We will help you size a GPU allocation for the models you want to serve and the jobs you want to run.