Distributed fine-tuning pipelines for any scale.
From a quick LoRA fine-tune on a single GPU to a full pre-training run across thousands of GPUs — CogniCloud Training Jobs orchestrates distributed workloads with automatic fault tolerance, elastic scaling, and experiment tracking built in.
Fully Sharded Data Parallel and Distributed Data Parallel configurations are generated automatically from your model architecture and cluster size.
Node failures trigger automatic checkpoint restoration and worker replacement. Long-running runs survive hardware failures with at most 5 minutes of lost work.
Scale worker count up or down mid-run without stopping the job. Useful for ramping up after the initial debugging phase or reacting to spot-instance preemptions.
Native integration with Weights & Biases, MLflow, and CogniCloud's own dashboard. Every run is logged with hyperparameters, metrics, and artifact checksums.
Efficient fine-tuning adapters trained on a fraction of the parameters. Combine with quantisation to fine-tune 70B+ models on a single 8× GPU node.
Before submitting a job, the scheduler provides a cost estimate based on your config, model size, and target GPU SKU. No billing surprises.
| Parallelism strategies | FSDP, DDP, TP, PP |
| Max nodes | Unlimited (contact for large clusters) |
| Fault tolerance | Auto-checkpoint & resume |
| Checkpoint interval | Configurable, default 5 min |
| LoRA / QLoRA | Supported (HuggingFace PEFT) |
| Experiment tracking | W&B, MLflow, native |
| Spot instance support | Yes, with auto-resume |
| Pricing model | Custom — contact us |
Training Jobs is currently in development — estimated Q3 2026.
No pricing yet. We offer tailored solutions only.
CogniCloud is in active development. Join the waitlist to get early access and stay updated on our roadmap. No pricing yet — we'll work with each team to find the right fit.
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