Gemma 3 VRAM Requirements: 1B vs 4B vs 12B vs 27B

Short answer: Google’s official Gemma 3 QAT Q4_0 GGUF model files scale from 1GB for 1B to 3.16GB for 4B, 8.07GB for 12B and 17.2GB for 27B. The 4B, 12B and 27B repositories also list separate 851–858MB FP16 vision projectors. Those file sizes are a starting floor—not complete VRAM requirements—because cache, runtime buffers, images and other allocations still need memory.

Official Gemma 3 QAT Q4_0 files compared

Gemma 3 model	Official model file	Official vision projector	Cautious starting lane
1B IT	1GB	None listed; text-only model	2GB+ available memory after runtime validation
4B IT	3.16GB	851MB	6GB for practical margin; 4GB is a constrained text-only experiment
12B IT	8.07GB	854MB	12GB starting tier; 16GB gives more useful margin
27B IT	17.2GB	858MB	24GB starting tier; 32GB gives more useful margin

Which Gemma 3 size should you choose?

Gemma 3 1B: smallest text-only path

Google labels the 1B variant as text-to-text, unlike the image-text-to-text 4B, 12B and 27B models. Its official QAT Q4_0 file is 1GB and the repository does not list a vision projector. Choose it when memory and deployment size matter more than using the larger family members; do not infer equal quality from fit. Read the exact Gemma 3 1B VRAM guide.

Gemma 3 4B: compact multimodal path

The official 4B QAT model is 3.16GB and its projector is 851MB. A nominal 4GB pool is therefore not a comfortable complete multimodal budget. Text-only may be possible under a tightly controlled runtime, while 6GB or 8GB provides more room for cache and buffers. Read the exact Gemma 3 4B VRAM guide, or compare the broader 3B/4B class.

Gemma 3 12B: mid-tier multimodal path

The 8.07GB model file does not make 8GB a whole-workload fit. Twelve gigabytes is the first common tier with some room beyond the file; 16GB is a more flexible target for context and multimodal allocations. Read the exact Gemma 3 12B VRAM guide. The family has no 14B member, so use the 13B/14B guide only as a nearby capacity comparison.

Gemma 3 27B: high-capacity multimodal path

The 17.2GB QAT model file is already larger than a nominal 16GB pool. That makes 24GB the first common one-device starting tier and 32GB the more flexible choice for additional working margin. Read the exact Gemma 3 27B VRAM guide for the complete capacity decision.

Text-only and multimodal memory are different

For 4B, 12B and 27B, Google’s repository separates the main model from an FP16 multimodal projector. A runtime may load the projector only for image support, but image preprocessing, encoded image tokens and runtime-specific buffers can add further allocations. Test text-only and image-plus-text workloads separately instead of adding two file sizes and calling the result peak VRAM.

Context limits are not fit guarantees

Google’s model card distinguishes the 1B text-only variant from the multimodal larger variants and documents different context capabilities across the family. Maximum context is an architectural capability, not proof that the maximum fits on a particular GPU. Gemma 3 uses hybrid local/global attention, so a naive full-attention cache formula across every layer is not an exact measurement. Runtime cache implementation, cache precision, prompt length, output length, images and concurrency all matter.

Memory-tier decision map

Available memory	Most plausible official QAT starting points	What to verify
2–4GB	1B; constrained 4B text-only experiments near the upper end	Runtime overhead and actual available—not nominal—memory
6–8GB	4B with more working room	Vision projector, context and display use
12GB	12B Q4 starting lane	8.07GB file plus cache/runtime/image allocations
16GB	12B with more margin	Longer context, concurrency and multimodal peak use
24GB	27B Q4 starting lane	Text versus vision path and representative context
32GB+	27B Q4 with more workload margin	Do not translate spare capacity directly into speed or quality

For cross-family planning, use the 8GB/12GB/16GB guide, model-size map or quantization guide.

Reproducible validation checklist

1. Choose the exact Google repository and accept the Gemma usage license.
2. Record the model filename, displayed size and checksum.
3. Decide whether the workload is text-only or image-plus-text.
4. Record runtime, version, GPU/offload settings and cache precision.
5. Test representative prompt length, output length, image count and concurrency.
6. Record peak memory, time to first token, throughput and any fallback.
7. Evaluate output quality before treating a smaller model or quantization as acceptable.

Use the workload test and submit a measured result.

Official sources

• Google Gemma 3 1B IT QAT Q4_0 GGUF files
• Google Gemma 3 4B IT QAT Q4_0 GGUF files
• Google Gemma 3 12B IT QAT Q4_0 GGUF files
• Google Gemma 3 27B IT QAT Q4_0 GGUF files
• Google Gemma 3 model card
• HighVRAM methodology

Reviewed October 25, 2026. Repository display values can change. HighVRAM has not independently benchmarked every model, device or runtime. File size does not equal peak memory, and fit does not establish speed or quality.

Cross-family vision comparison: the multimodal LLM VRAM guide places Gemma 3 beside Llama 3.2 Vision and Mistral Small 3.2 with complete projector/encoder memory paths.