Fine-tune custom assistantswithout building infrastructureby hand
Bring your data, choose a foundation model, and let LLMTune handle training, monitoring, and deployment.
Federated or Traditional compute.
No infrastructure. No limits. Your choice.
Choose your compute model
Traditional or Federated. Single Instance or GPU Cluster. All supported. All ready. All codeless.
Traditional Computing
Decentralized traditional compute
Train your models using traditional compute infrastructure. Single location, predictable performance, full control. Perfect for stable workloads and on-premise needs.
Deployment Options
One GPU instance. Perfect for smaller models, testing, and development. Fast setup, predictable costs.
Multiple GPUs working together. Scale training across multiple devices. Faster training for large models.
- Single location
- Predictable performance
- Full control
- Instance or Cluster
Best for: Stable workloads, on-premise needs, predictable costs
Federated Computing
Distributed compute across global nodes
Train your models using distributed compute across global nodes. Privacy-preserving, unlimited scale, lower costs. Perfect for privacy-sensitive data and global scale training.
Deployment Options
One GPU instance from the federated network. Privacy-preserving, distributed compute. Perfect for sensitive data.
Multiple GPUs from different global nodes working together. Unlimited scale, faster training, lower costs.
- Global distribution
- Privacy-preserving
- Unlimited scale
- Instance or Cluster
Best for: Privacy-sensitive data, global scale, lower costs
One platform. All compute types. All deployment options.
Choose Traditional or Federated. Select Single Instance or GPU Cluster. Switch anytime. No infrastructure setup. No code required.
All compute types supported
Choose Traditional, Federated, or Clustering. Switch anytime.
Ready infrastructure
No setup required. Start training in minutes.
Codeless interface
No-code workflows. Guided setup. Automatic configuration.
Unified platform
One platform. All compute types. Seamless integration.
Everything you need to train
Guided workflows. Real-time monitoring. One-click deployment. All training methods. All modalities.
Guided fine-tunes
Upload data, pick a base model, and launch LoRA/QLoRA or full runs with safe defaults. No-code interface. Real-time monitoring.
Live telemetry
Track tokens/sec, loss, and spend in real time without wiring extra dashboards. Automatic checkpoints. Production-ready metrics.
One-click promotion
Push the best checkpoint live, issue scoped keys, and test in the Playground instantly. Deploy OpenAI-compatible APIs.
How it flows
Three simple steps from dataset to production. No infrastructure. No complexity. Just results.
Prep data
Upload JSONL/CSV or connect stores. Mask and version in minutes. Quality scoring. PII detection. Automatic cleaning.
Tune & monitor
Choose SFT, DPO, PPO, RLAIF, CTO, or any method. Tweak knobs, and watch metrics stream. Federated or Traditional compute.
Ship & iterate
Promote the winner, share the endpoint, and keep iterating from the same workspace. OpenAI-compatible deployment.
Model capability overview
Scan the core fine-tuning lanes and see which model families are supported. All modalities. All methods.
Text-to-text
Instruction copilots, support agents, knowledge search. Train any text model with any method.
Supported model examples
Image-to-text
Captioning, screenshot QA, product intelligence. Vision-language models with text and images.
Supported model examples
Audio Understanding
Train models to understand and reason about audio content, speech, music, and sounds.
Supported model examples
Audio-to-text (ASR)
Meeting transcription, contact center automation, voice analytics. High-accuracy speech recognition.
Supported model examples
Video-to-text
Long-form video understanding, compliance reviews, ops monitoring. Video-language reasoning.
Supported model examples
Code (text → code)
Developer copilots, CI assistants, automated refactors. Train models that code, reason, and act.
Supported model examples
Multimodal (text + image → text)
Unified copilots that can understand documents and UI screenshots. Multimodal reasoning at scale.
Supported model examples
Text-to-audio (TTS)
Voice generation, product voices, multilingual narration. High-quality speech synthesis.
Supported model examples
Text-to-embeddings
Retrieval, semantic search, embeddings for analytics. Train embedding models for RAG applications.
Supported model examples
Ready to fine-tune?Launch Studio now
Start training your custom AI models in minutes. No infrastructure setup. No code required.