I built MOLT: a local fine-tuning system with fit tests, checkpoints, and deployment tracing

I’ve been building MOLT, a local fine-tuning system for consumer NVIDIA GPUs.

The goal is not only to start a QLoRA run. It is to make the whole process from model and dataset to a working local adapter less fragile.

MOLT currently handles:

\- dataset detection, preparation, and validation

\- GPU, VRAM, system-RAM, storage, and thermal checks before a run

\- automatic microbatch fit testing

\- 4-bit NF4 QLoRA training with BF16 adapters

\- safe checkpoints with integrity checks and proper resume state

\- telemetry for VRAM, temperature, energy, clocks, and throughput

\- base-vs-adapter evaluation

\- local adapter chat, export/GGUF workflows, and runtime diagnostics

Under the hood, I’m also experimenting with custom CUDA/Triton kernel and runtime paths, memory planning, CUDA graphs, replay modes, and fused optimization where the hardware and shape are qualified.

On my RTX 4060 Laptop 8GB, a recent Qwen 3B training run reached about 1,005 training targets/sec end to end very close to my historical Unsloth run at about 1,007 targets/sec. That is not a broad “MOLT beats Unsloth” claim yet; I still need repeated, matched quality/energy/memory tests.

What I want MOLT to become is a reliable local fine-tuning environment: you bring the model and dataset; it checks the plan, runs safely, preserves the evidence, and helps verify the adapter actually works after deployment.

I’m looking for people who genuinely fine-tune on a single NVIDIA GPU and would be willing to test it. What would MOLT need to support before you would try it?