Gemma 3 4B VRAM Requirements: 4GB, 6GB or 8GB?

Short answer: Google’s official Gemma 3 4B instruction-tuned QAT Q4_0 GGUF model file is 3.16GB. Its repository also lists a separate 851MB FP16 vision projector. A nominal 4GB pool is therefore a constrained text-only experiment, not a comfortable complete multimodal budget. 6GB is the first practical starting tier for this exact artifact; 8GB provides more useful context and runtime margin.

Gemma 3 4B capacity verdict

Available memory	Official QAT Q4_0 path	Practical interpretation
4GB	Constrained text-only experiment	The 3.16GB file leaves only about 0.84GB before cache, buffers, kernels and display use; model plus projector slightly exceeds 4GB before other allocations.
6GB	First practical starting tier	More plausible for controlled text or vision tests, but context and runtime still require validation.
8GB	More flexible Q4 tier	Adds useful working room for context, images, batching and runtime variation.
12GB	Substantial Q4 headroom	Useful for heavier workloads or adjacent model tests; spare memory does not establish speed.

What Google publishes

The official Google repository tree lists gemma-3-4b-it-q4_0.gguf at 3.16GB and mmproj-model-f16-4B.gguf at 851MB. Google describes this as a quantization-aware-trained Q4_0 release. Those values support capacity planning for this artifact; they do not make every community 4-bit format equivalent in size or quality.

Why 4GB is constrained

The model file consumes roughly 79% of a nominal 4GB label before runtime state. Adding the displayed projector takes the two files to about 4.01GB, still before cache, image processing, buffers or display use. Some runtimes can offload part of the workload to system memory, but that is not the same as a full-GPU fit and can change performance materially.

6GB versus 8GB

• Choose 6GB when the official QAT Q4_0 artifact is the target and you will control context, images and concurrency.
• Choose 8GB when you want a more forgiving text/vision path and margin for runtime-specific allocations.
• Choose 12GB when the real workload needs longer context, more concurrency or room to test the larger 12B class.

Compare the broader class in the 3B/4B requirements guide or contrast an exact dense alternative in the Qwen3-4B guide.

Text-only and image-plus-text are different tests

Path	Account for	Representative test
Text-only	3.16GB model, cache, buffers and system/display use	Real prompt length, output target and concurrency
Image + text	Model, 851MB projector, image processing and runtime allocations	Real image count/resolution with the same output target

128K capability is not a 6GB or 8GB promise

Google’s Gemma 3 model card documents a 128K-token context capability for the 4B model. A local runtime must still allocate its cache and working buffers. Gemma 3 uses hybrid local/global attention, so a naive full-attention formula across every layer is not an exact memory estimate. Cache implementation and precision, prompt length, generated tokens, images, batching and concurrency all affect the result.

A reproducible fit test

1. Record the exact Google repository, filename, checksum and license acceptance.
2. Measure available memory before loading; do not rely only on the device label.
3. Record OS, driver, runtime version and GPU/offload settings.
4. Test representative prompt length, output length and concurrency.
5. Repeat vision tests with the official projector and representative images.
6. Record peak memory, time to first token, throughput and any fallback.
7. Evaluate output quality before deciding this quantization meets the task.

Use the workload test and share a measured result.

Official sources and limits

• Google Gemma 3 4B IT QAT Q4_0 GGUF file tree
• Google Gemma 3 model card
• Google Gemma documentation
• HighVRAM methodology

Reviewed October 25, 2026. File sizes are the repository display values on that date. HighVRAM has not independently benchmarked every tier or runtime. File size does not equal peak VRAM, and fit does not establish speed or output quality.

Need a smaller text-only path? Read the Gemma 3 1B VRAM guide for Google’s official 1GB QAT file and 1GB/2GB/4GB planning.