Written by Jakub Rusinowski · Last updated July 10, 2026
Gemma 3 delivers exceptional quality-per-VRAM ratio, with the 4B model outperforming many 7B competitors. Features multimodal capabilities and a 128k context window across all sizes. Previous generation — superseded by Gemma 4 (E2B/E4B/26B-A4B/31B and the 12B Unified follow-up), which improves efficiency and multimodal support. Still widely used and supported.
| Gemma 3 1B Instruct | Min 1 GB VRAM · Q4_K_M · 32,000 ctx · ollama run gemma3:1b |
| Gemma 3 4B Instruct | Min 3 GB VRAM · Q4_K_M · 128,000 ctx · ollama run gemma3:4b |
| Gemma 3 12B Instruct | Min 8 GB VRAM · Q4_K_M · 128,000 ctx · ollama run gemma3:12b |
| Gemma 3 27B Instruct | Min 17 GB VRAM · Q4_K_M · 128,000 ctx · ollama run gemma3:27b |
The cheapest GPU that runs Gemma 3 locally (min 1 GB VRAM) is the Intel Arc B570 (10 GB).
Install Ollama then run: ollama run gemma3:1b
Minimum VRAM: 1 GB. For best results use Q4_K_M quantization.
Gemma 3 needs about 1 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Gemma 3 1B Instruct (1 GB, Q4_K_M); Gemma 3 4B Instruct (3 GB, Q4_K_M); Gemma 3 12B Instruct (8 GB, Q4_K_M); Gemma 3 27B Instruct (17 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Yes — Gemma 3 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 3 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 gemma3:1b. This downloads Gemma 3 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.