Bring your own training code.
Run pre-training, post-training, and LoRA fine-tuning with your code, data, and resource requirements.
TRAINING · INFERENCE · AGENTS
Train models, run agents, and call inference APIs. Bring your code and data. Nodus handles compute placement, job execution, and recovery from saved checkpoints.
Run your container on matched compute. Follow logs and collect outputs from the same job.



Nodus from your coding agent
Give Claude Code, Codex, or Cursor GPU access. Estimate a run, set a spending limit, follow logs, and retrieve results from your agent.
What you can run
Run pre-training, post-training, and LoRA fine-tuning with your code, data, and resource requirements.
Run agents in sandboxes, execute your evals, and support longer tasks with configurable limits.
Explore task environments, run training templates, and compare evaluation results.
Call hosted models through the OpenAI-compatible Nodus inference API. Inference is in Beta.
Your research, around the workload
Keep saved project files between compute sessions. Pick up your research through supported VS Code, Jupyter, or SSH connections.
Explore workspaces ↗Pricing
Hourly supply price
$2.600/ hourH100 SXM · 80 GB VRAM
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Hourly supply price
$0.0357/ hour2 vCPU · 4 GiB RAM · 80 GiB disk
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Why Nodus
From finding compatible compute to restoring saved files, Nodus manages the infrastructure around your job.
Specify your container image, memory, and compute requirements. Nodus finds compatible capacity for the job.
How matching works →Track usage and spending across setup, execution, and recovery, with costs tied to each workload.
Set limits for agent runs →When a run is interrupted, Nodus restores saved files on replacement compute. Your code reloads its checkpoint and continues.
Make training resumable →Across Nodus
Reserved compute for your workload. Agents that make AI more efficient. A new look at how compute is valued.
Reserved GPU capacity, supplied by Nodus. Request dedicated GPUs or a connected cluster for your configuration, location, and timeline.
Explore reserved capacity↗An agentic research lab. Agents write code, run experiments, and evaluate ways to lower the cost of training, RL, and inference.
Explore research↗Explore the financial infrastructure for institutional compute and inference.
Explore the concept↗Explore the platform
Run your training code with your own data, dependencies, and compute requirements.
Give agents a sandbox to execute tools, work with files, and run evaluations. Set time and spending limits for longer tasks.
Choose a task environment, run a training template, and evaluate the results. Iterate on your policy in one place.
Call models through an OpenAI-compatible API. Stream responses and track token usage for each request.