Google’s Gemma 3 is one of the most accessible open-weight model families for local inference. The 4B variant runs on almost anything with 8GB VRAM. The 12B hits a strong quality-to-VRAM ratio. And the 27B, while demanding, fits on a single RTX 4090. Here is exactly what you need for each size.
NVIDIA GeForce RTX 4090
24GB GDDR6X24GB VRAM runs Gemma 3 27B at Q4_K_M comfortably. The most capable single GPU for local Gemma 3 inference.
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Quick answer
- Gemma 3 4B: RTX 4060 (8GB) or better — anything with 8GB+ VRAM
- Gemma 3 12B: RTX 4060 Ti 16GB — 16GB VRAM minimum for comfortable inference
- Gemma 3 27B: RTX 4090 (24GB) — 24GB needed at Q4; 5090 for Q5+
Gemma 3 VRAM requirements
| Model | Q4_K_M Size | Min VRAM | Comfortable VRAM |
|---|---|---|---|
| Gemma 3 4B | ~2.5GB | 6GB | 8GB+ |
| Gemma 3 12B | ~7.5GB | 10GB | 16GB |
| Gemma 3 27B | ~16.5GB | 20GB | 24GB |
Gemma 3 models are on the lean side for their parameter counts — Google’s training efficiency means the 27B model is lighter than many competing 20B models at the same quantization.
VRAM capacity memory bandwidth Specs are manufacturer figures. Bar lengths are scaled independently per metric.
Performance benchmarks by GPU
Ollama at Q4_K_M, with throughput modelled from memory bandwidth (how and why):
| GPU | Gemma 3 4B | Gemma 3 12B | Gemma 3 27B |
|---|---|---|---|
| RTX 5090 (32GB) | ~200 tok/s | ~70 tok/s | ~38 tok/s |
| RTX 4090 (24GB) | ~130 tok/s | ~48 tok/s | ~24 tok/s |
| RTX 4060 Ti 16GB | ~75 tok/s | ~28 tok/s | Won’t fit |
| RTX 4060 (8GB) | ~42 tok/s | Won’t fit | Won’t fit |
| RTX 3090 (24GB, used) | ~95 tok/s | ~35 tok/s | ~18 tok/s |
| RTX 3060 12GB (used) | ~38 tok/s | Won’t fit | Won’t fit |
The RTX 4090’s 24GB headroom is what makes Gemma 3 27B practical. The 4060 Ti 16GB falls just short for the 27B variant — the model’s weights plus KV cache push past 16GB under load.
GPU picks by Gemma 3 model size
Gemma 3 4B — almost anything works
The 4B model at Q4 is ~2.5GB. Any GPU with 6GB+ VRAM can load it. Even an RTX 4060 (8GB) gives you 42 tok/s, which is fast for interactive use. If you are only running the 4B model, there is no reason to spend more than $479.
Gemma 3 12B — the sweet spot model
The 12B is where Gemma 3 gets interesting. At Q4_K_M it needs 10GB, and a 16GB card gives you comfortable headroom for context. The RTX 4060 Ti 16GB ($425) is the natural fit — 28 tok/s at Q4 is smooth for chat and coding tasks. The 12B model at Q5 on this card (~22 tok/s) is still usable if you want better output quality.
Gemma 3 27B — needs a 24GB card
The 27B requires ~16.5GB for weights alone. Factor in KV cache for 4K context and you hit ~18–20GB total. The RTX 4090 at 24GB handles this with room to spare (~24 tok/s at Q4), and the RTX 3090 (used, 24GB) runs it at ~18 tok/s for those watching budget. For Q5_K_M or Q6 on the 27B, the RTX 5090’s 32GB is the only consumer card that fits.
Which GPU should YOU buy?
RTX 4060 (8GB) — Gemma 3 4B only. Great for getting started locally without heavy investment.
RTX 4060 Ti 16GB (~$425) — The best bang for buck for Gemma 3. Runs 4B blazing fast and 12B comfortably. The right choice for most users.
RTX 4090 (~$2,200) — The only way to run Gemma 3 27B on a single consumer card. Runs the full model family from 4B to 27B at quality quantizations.
RTX 3090 (used) (~$800) — Same 24GB VRAM as the 4090 at roughly a third of the price. Slower tok/s but fits the 27B. Best value if you need 27B on a budget.
RTX 5090 (~$4,900) — For Gemma 3 27B at Q5/Q6 or the newer Gemma 4 models that push VRAM requirements further. Future-proofed but expensive.
Common mistakes to avoid
- Trying Gemma 3 27B on a 16GB card. The model does not fit at any practical quantization on 16GB. The 4060 Ti 16GB is excellent for 12B but cannot touch 27B.
- Over-buying for the 4B model. If Gemma 3 4B covers your use case, an RTX 4060 or even older 8GB card handles it fine. No need for a 24GB card to run a 2.5GB model.
- Ignoring quantization quality trade-offs. Running Gemma 3 12B at Q3 to fit on 8GB cards gives significantly worse output than Q4 on a 16GB card. The quality drop is noticeable for reasoning tasks.
- Forgetting Gemma 3 is multimodal. The vision-language variants use more VRAM than text-only versions. Budget ~2–3GB extra VRAM if you plan to use image inputs.
Final verdict
| Your goal | Best GPU | Price |
|---|---|---|
| Gemma 3 4B only | RTX 4060 | ~$479 |
| Gemma 3 12B (best value) | RTX 4060 Ti 16GB | ~$425 |
| Gemma 3 27B (budget) | RTX 3090 (used) | ~$800 |
| Gemma 3 27B (best speed) | RTX 4090 | ~$2,200 |
| Future-proofed | RTX 5090 | ~$4,900 |
The RTX 4060 Ti 16GB is the most practical Gemma 3 GPU for most users — it covers the 4B and 12B models excellently and represents an affordable entry point to quality local inference.
Check NVIDIA GeForce RTX 4060 Ti 16GB on Amazon→Buy on Shopee SG→ Check NVIDIA GeForce RTX 5090 on Amazon→Buy on Shopee SG→For the full Gemma model lineup history, see our best GPU for Gemma guide. Running Gemma 3 through Ollama? Our best GPU for Ollama article has setup tips. For a full VRAM-to-model-size reference, see how much VRAM for local LLM.