Written by Jakub Rusinowski · Last updated July 10, 2026
Google DeepMind's breakthrough open-weight model. Gemma 4 27B delivers GPT-4 level performance at 14 GB VRAM, making frontier-quality AI accessible on consumer GPUs. Features a hybrid architecture with interleaved local and global attention, multi-image understanding, and 128k context window. Achieves 85 tokens/second on RTX 4090.
| Gemma 4 4B | Min 4 GB VRAM · Q4_K_M · 128,000 ctx · ollama run gemma4:4b |
| Gemma 4 12B | Min 8 GB VRAM · Q4_K_M · 128,000 ctx · ollama run gemma4:12b |
| Gemma 4 27B ⭐ | Min 14 GB VRAM · Q4_K_M · 128,000 ctx · ollama run gemma4:27b |
The cheapest GPU that runs Gemma 4 (Legacy Listing — Unverified) locally (min 4 GB VRAM) is the Intel Arc B570 (10 GB).
Install Ollama then run: ollama run gemma4:4b
Minimum VRAM: 4 GB. For best results use Q4_K_M quantization.
Gemma 4 (Legacy Listing — Unverified) needs about 4 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Gemma 4 4B (4 GB, Q4_K_M); Gemma 4 12B (8 GB, Q4_K_M); Gemma 4 27B ⭐ (14 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — Gemma 4 (Legacy Listing — Unverified) runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.
Q4_K_M is the best balance of quality and VRAM for Gemma 4 (Legacy Listing — Unverified) in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
Install Ollama, then run: ollama run gemma4:4b. This downloads Gemma 4 (Legacy Listing — Unverified) and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.