Gemma 4 Can Now Be Fine-Tuned on 8GB VRAM With Unsloth | Local AI
Unsloth has released support for fine tuning Google's Gemma 4 E2B model on consumer GPUs with just 8GB VRAM, running 1.5x faster with 60% less memory than stan…
Published on MyPrivateClaw
Apr 8, 2026, 8:58 AM UTC
Coverage date
Apr 8, 2026
Last updated
Apr 8, 2026, 12:59 PM UTC
News summary
Fine Tuning Frontier Models on Consumer Hardware Unsloth, the open source fine tuning optimization library, has released full support for Google's Gemma 4 model family — including a configuration that enables fine tuning the Gemma 4 E2B (2 billion parameter) model on GPUs with just 8GB of VRAM. This puts Gemma 4 fine tuning within reach of consumer hardware like the RTX 3080, RTX 4070, and M2/M3 MacBooks with 16GB unified memory. The release supports all four Gemma 4 variants: E2B, E4B, 26B A4B, and 31B. Unsloth reports training speeds approximately 1.5x faster than Flash Attention 2 (FA2) setups, with approximately 60% less VRAM consumption — and no accuracy loss. Hardware Requirements at a Glance Model Training Method Minimum VRAM Gemma 4 E2B LoRA 8 10 GB Gemma 4 E4B LoRA 10 GB Gemma 4 E4B Full fine tune 17 GB Gemma 4 26B A4B LoRA 40+ GB Gemma 4 31B QLoRA 22 GB Bug Fixes That Matter B…