v3.2 — Distributed training on 512 GPUs now in GA

Train Faster. Deploy
Anywhere. Ship Models That Scale.

Zorvix is the ML platform that handles the infrastructure so your team can focus on the science. Distributed training, experiment tracking, automatic hyperparameter tuning, and one-click model serving — all in one place.

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PyTorch & TensorFlow native

Kubernetes under the hood

No vendor lock-in

train_model.py
# Zorvix SDK — 3 lines to distributed training

                import
                Zorvix
                as
                ns
              

                run =
                ns.init({
              
project: "vision-transformer-v4",
gpu_count: 64,
gpu_type: "A100-80GB",
auto_tune: True,
checkpoint_every: 500
})

                run.log({
              
loss: loss.item(),
accuracy: acc,
lr: scheduler.get_last_lr()[0]
})

                run.register_model(
                "vit-v4-prod",
              

                stage="production")
              

                ●
                Epoch 18/50 — loss: 0.0821 — acc: 94.3%
              

              
                Experiment Tracker — vision-transformer-v4
              
            
run_042
lr=3e-4 · batch=256
94.3%
run_041
lr=1e-4 · batch=256
91.1%
run_040
lr=1e-3 · OOM error
Failed

Platform Capabilities

Every Tool Your ML Team Needs,
In One Unified Platform

Zorvix eliminates the infrastructure tax that slows down ML teams. Focus on model development while we handle compute orchestration, data pipelines, and deployment.

Distributed Training at Any Scale

Scale from a single A100 to 512-GPU clusters with one config change. Zorvix handles data sharding, gradient synchronization, fault tolerance, and automatic checkpointing. Train LLMs with 70B+ parameters without writing a line of distributed systems code.

Experiment Tracking & MLflow-Compatible

Log metrics, hyperparameters, and artifacts with a single SDK call. Compare experiments in the visual dashboard, reproduce any run with one click, and share results with stakeholders via shareable run URLs.

Automatic Hyperparameter Optimization

Define your search space and let Zorvix's Bayesian optimizer find the best hyperparameters automatically. 10× more efficient than grid search, with parallel trial execution across your GPU budget.

Model Registry & Versioning

Centralized model registry with semantic versioning, approval workflows, and stage management (Staging → Production). Every model is linked to its training run, dataset version, and evaluation results for full lineage.

One-Click Model Serving

Deploy any registered model as a REST endpoint or streaming API. Auto-scales from zero to thousands of requests per second. Supports A/B testing between model versions with traffic splitting and canary deployments.

Production Monitoring & Drift Detection

Continuous monitoring of model performance, data distribution shifts, and prediction confidence in production. Automatic alerts when drift is detected, with one-click retraining triggers to keep your models sharp.

Workflow

From Notebook to Production in 4 Steps

Connect Data Sources

Link S3 buckets, GCS, Azure Blob, or Hugging Face datasets. Zorvix handles versioning and efficient loading for multi-hundred-GB datasets automatically.

Configure Training Run

Define your model, hyperparameters, and compute requirements in a simple YAML config or via our Python SDK. Launch distributed training with one command.

Evaluate & Register

Run evaluation suites against held-out test sets. Compare against baseline models. When metrics pass your thresholds, promote the model to the registry with a single API call.

Deploy & Monitor

Deploy to our managed inference cluster or export to Kubernetes, AWS SageMaker, or any container environment. Monitor performance and trigger retraining automatically on drift detection.

From ML Engineers

Built by Researchers, for Researchers

"Zorvix cut our model training iteration cycle from 4 days to 6 hours. The experiment tracking dashboard alone replaced our entire homegrown MLflow setup. The distributed training just works — I don't think about infrastructure anymore."

Dr. Kenji Tanaka

Principal ML Engineer · Prismatic Research

"The hyperparameter optimization found a learning rate schedule we never would have tried manually — it boosted our model's F1 score by 3.8 points. That's the difference between beating and losing to the competition in our use case."

Léa Beaumont

Research Scientist · Comet ML Labs

"We migrated from SageMaker to Zorvix in a week. The cost savings were immediate — 40% cheaper compute through spot instance management plus zero DevOps headcount for ML infrastructure. The model serving latency is also significantly better."

Amir Okonkwo

VP AI Engineering · DataStream

Pricing

Pay for Compute, Not the Platform

Zorvix charges only for GPU compute. The platform, tracking, registry, and monitoring are free. No per-seat fees, no per-experiment charges.

Researcher

Free + compute

For individual researchers and students. Full platform access with community GPU pool.

Unlimited experiments
Up to 4× A100 per run
$0.90/GPU-hour compute
50 GB model storage
Community support

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Common Questions

Yes. Zorvix is framework-agnostic. We provide native SDKs for PyTorch, TensorFlow, JAX, and Hugging Face Transformers. Any Python-based training script can be adapted in minutes. We also support custom Docker containers if you need specific system dependencies.

We offer NVIDIA A100 (40GB and 80GB), H100 (80GB), and A10G GPUs. Compute is billed by the second once your job starts. Spot instances are available at 60% discount with automatic checkpointing to handle preemption. Reserved capacity options are available on Enterprise plans.

Yes, on Enterprise plans. The Zorvix Agent can be deployed on your existing Kubernetes cluster, on-premise GPU servers, or private cloud. All platform features — experiment tracking, model registry, serving — work with your own compute. This is a popular option for regulated industries.

SageMaker and Vertex AI are cloud-specific and tie you to their ecosystem. Zorvix is cloud-agnostic, significantly simpler to use, and typically 30–50% cheaper on compute thanks to our spot instance management and efficient scheduling. Unlike those platforms, we're built for ML teams, not cloud architects.

In multi-node training, you designate a logging rank (typically rank 0). All metrics logged from that process are automatically aggregated and displayed as a single experiment run. The SDK handles coordination so you don't have to manage distributed logging manually. Full support for gradient and activation histograms across all ranks.

Train Faster. Deploy Anywhere. Ship Models That Scale.

Every Tool Your ML Team Needs,In One Unified Platform