LLM Fine-Tuning Pipeline with Unsloth — 2x Faster, 50% Less VRAM
A production LLM fine-tuning pipeline built on Unsloth — we fine-tune domain-specific LLMs on a 4× L4 GPU machine with 2x speed-up and ~50% VRAM reduction vs baseline, plus an evaluation harness that blocks quality regressions.
What AI platform client was up against
Fine-tuning LLMs on domain data is a common ask, but full fine-tuning is prohibitively expensive, and naive LoRA runs often silently regress on general-purpose capabilities. The client needed a reproducible pipeline where a data scientist could queue a new fine-tune, get it done on one or two GPUs instead of eight, and receive a confidence-scored evaluation report before the model ever reached production.
What we built
We built the pipeline on top of Unsloth for its 2x training speed and ~50% VRAM reduction — fine-tunes that would normally need a multi-H100 rig now land on a single 4× L4 machine. The pipeline wires into a managed training orchestrator with data versioning, experiment tracking (Weights & Biases), and a multi-axis eval harness (domain task performance + general-purpose regression checks + safety probes). Fine-tunes auto-publish to a model registry with QLoRA adapters, ready for deployment via vLLM. Every run produces a one-page report: deltas on domain benchmarks, regression flags, training loss curves.
What shipped
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