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Gemma 4 12B Fine-Tuned Locally on 8GB VRAM in Chess Demo

June 15, 2026 at 11:00 EDT

Open Source
Foundation Models
Software Dev & Coding

A community demonstration shows how to fine-tune Google DeepMind's open-weight Gemma 4 12B entirely locally on an 8GB-class GPU to predict the best chess move. It illustrates that custom models handling text, images and audio can be built on one's own hardware, drawing attention among developers.

April 2, 2026 · Gemma 4 · Open Weights

Fine-Tuning a 12B Multimodal Model on Just 8GB of VRAM

A community demo trains Google DeepMind's open-weight Gemma 4 12B entirely on a consumer GPU — using LoRA on a chess-move dataset to show serious custom models can be built on a home PC, even Colab's free tier.

8GB

VRAM to fine-tune locally

256K

Tokens of context

140+

Languages, plus text · image · audio

Inference VRAM: BF16 vs 4-bit quantized

Quantizing 12B to 4-bit (Q4_K_M) shrinks the footprint to fit an RTX 4060 8GB.

~24GB

12B BF16

8-10GB

12B 4-bit (Q4_K_M)

Takeaway: roughly a 3× cut in VRAM moves a 12B model from datacenter GPUs onto a home card.

Fine-tuning VRAM guidelines (LoRA via Unsloth)

E2B LoRA

8-10GB

E4B LoRA

10-17GB

31B dense

~22GB

Why Unsloth makes it fit

Versus FlashAttention 2, the same LoRA run trains faster and uses far less memory.

1.5×

faster training

~60%

lower VRAM

mins

E2B chess demo training time

Encoder-free design → single-pass tuning

No separate vision/audio encoders to freeze — all weights update at once, so LoRA & QLoRA work better.

Text · Image · Audio input

→

Linear projection (unified)

→

All weights tuned in one pass

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