Private On-Premise Fine-Tuning
Train model weights directly on your domain vocabulary, document templates, and proprietary data. You retain 100% ownership of the final model files. Zero training data touches external cloud GPUs.
The Three Stages of Private Fine-Tuning
Baseline Evaluation
We benchmark standard open models (Qwen3.8-27B, GLM 5.2, Gemma 4) against your domain tasks to establish zero-shot and few-shot accuracy baselines.
On-Premise LoRA / QLoRA
We execute Parameter-Efficient Fine-Tuning (PEFT) on dedicated local GPUs using your curated dataset, preserving base capabilities while mastering your target domain.
Quantized Serving
We merge adapter weights, quantize to NVFP4, FP8, or Q8 GGUF, and deploy onto your DGX Spark, RTX PRO workstation, or local cluster for real-time serving.
Why Fine-Tune Locally?
100% Weight Ownership
The resulting Safetensors or GGUF adapter files are assets on your drive. You are never dependent on a cloud vendor's hosted fine-tuning endpoint or deprecation schedule.
Domain Vocabulary Precision
Teach open models exact legal citation formats, medical taxonomy, proprietary JSON schemas, or internal API structures that base models frequently hallucinate.
Air-Gapped Training Data
Your training datasets (unredacted contracts, patient charts, proprietary codebase commits) never leave your local subnet during training or inference.
Pair Fine-Tuned Models with Custom Agents
A fine-tuned model provides domain precision; a custom agent provides tool execution and vector search. Combine both for end-to-end automation.
Discuss Your Fine-Tuning Dataset
Tell us about your target domain and model performance goals.
Request Fine-Tuning Consultation