Litesearch: Karpathy's autoresearch but for consumer GPUs (4–8GB) + easy GUI

Reddit r/LocalLLaMA / 3/22/2026

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Key Points

Litesearch is a fork of Karpathy's autoresearch designed to run tiny LLM experiments on consumer GPUs with 4–8 GB of VRAM.
It automatically selects model size, depth, batch, and sequence length to fit VRAM while leaving a buffer to prevent OOM errors.
It provides a simple dark GUI dashboard showing live VRAM usage, logs, and a config preview, eliminating the need to stare at a terminal.
It strips back fancy kernels (uses torch sdpa), offers easier setup, and works on older Pascal GPUs.
The project is NVIDIA-only for now, with approximate parameter mappings (4GB ~86M, 8GB ~285M) and a MIT license with pip/uv installation.

Karpathy's autoresearch is awesome — agent edits train.py and runs tiny LLM experiments overnight. But it wants serious VRAM.

I forked it to run on normal cards like my 1080/3060:

Auto-picks model size/depth/batch/seq len so it fits your VRAM (leaves buffer, no more OOM surprises)
Simple dark GUI dashboard: live VRAM bar, logs, config preview, start/stop — no terminal staring
Stripped fancy kernels (uses torch sdpa), easier setup, works on older Pascal too

Quick table example (full in README):
4GB → ~86M params
8GB → ~285M params
(Currently NVIDIA-only and works on every of their GPUs)

(Props to Karpathy for the original idea.)

Reddit r/LocalLLaMA

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