From the Best GPU for LLM archive. The canonical version has interactive calculators, an up-to-date GPU comparison table, and live pricing.
Gemma 4 ships four variants, and the hardware gap between them is enormous. The E2B runs on a potato. The 31B Dense demands a flagship card. The real story is the 26B-A4B MoE — a model that delivers near-30B quality while fitting on a 16GB GPU. That is the variant most people should be targeting, and it changes the GPU calculus significantly.
See the recommended pick on the original guide
Quick answer by variant
- Gemma 4 E2B (~2B): Any GPU with 4GB+ VRAM — even integrated graphics can handle this
- Gemma 4 E4B (~4B): Any GPU with 6GB+ VRAM — RTX 3060 12GB is overkill
- Gemma 4 26B-A4B (MoE): RTX 4060 Ti 16GB or RTX 5070 Ti — 16GB VRAM fits Q4, and MoE means fast inference
- Gemma 4 31B Dense: RTX 4090 (24GB) minimum — RTX 5090 for Q5 or higher quantizations
VRAM requirements for every Gemma 4 variant
| Variant | Architecture | Q4_K_M | Q5_K_M | Min VRAM | Recommended |
|---|---|---|---|---|---|
| E2B (~2B) | Dense | ~1.5GB | ~2GB | 4GB | 6GB+ |
| E4B (~4B) | Dense | ~2.5GB | ~3.5GB | 6GB | 8GB+ |
| 26B-A4B | MoE (4B active) | ~14GB | ~18GB | 16GB | 16GB+ |
| 31B Dense | Dense | ~20GB | ~32GB | 24GB | 24GB+ |
The KV cache adds 2-4GB for longer conversations. On the 26B MoE, that means a 16GB card is tight at Q4 with long context — you may need to limit context to 4K-8K tokens. On the 31B Dense, the RTX 4090's 24GB leaves only 4GB of headroom after model weights, which is enough for moderate context but not 16K+ conversations.
VRAM chart available at the original article
Why the 26B-A4B MoE is the variant to watch
MoE (Mixture of Experts) means the model has 26B total parameters but only activates ~4B per forward pass. The result: you get 26B-class reasoning quality at 4B-class inference speed. On an RTX 5070 Ti (16GB), we measured ~70 tok/s at Q4 — faster than running a traditional 14B dense model on the same card.
The trade-off is memory. All 26B parameters still need to be loaded into VRAM, which is why it needs ~14GB at Q4 despite the low active parameter count. But 14GB fits cleanly on any 16GB card, making this the most capable Gemma 4 variant accessible to mid-range hardware.
See the recommended pick on the original guide
Performance benchmarks
Tested via Ollama at Q4_K_M:
| GPU | E2B | E4B | 26B-A4B MoE | 31B Dense |
|---|---|---|---|---|
| RTX 5090 (32GB) | ~250 tok/s | ~180 tok/s | ~110 tok/s | ~42 tok/s |
| RTX 4090 (24GB) | ~170 tok/s | ~120 tok/s | ~80 tok/s | ~28 tok/s |
| RTX 5070 Ti (16GB) | ~130 tok/s | ~90 tok/s | ~70 tok/s | Won't fit |
| RTX 4060 Ti 16GB | ~85 tok/s | ~60 tok/s | ~45 tok/s | Won't fit |
| RTX 3090 (24GB, used) | ~110 tok/s | ~75 tok/s | ~55 tok/s | ~20 tok/s |
| RTX 3060 12GB (used) | ~45 tok/s | ~32 tok/s | Won't fit | Won't fit |
The 26B MoE's speed advantage is clear — it runs faster than the 31B Dense on the same GPU despite similar total parameter counts. This is the MoE payoff in action.
GPU picks by budget
Interactive decision flow available at the original article
Under $200: E2B and E4B only
The RTX 3060 12GB (used, ~$150) handles both small variants with ease. 45 tok/s on the E2B is instant. If you only need lightweight Gemma 4, this is the floor.
See the recommended pick on the original guide
$400-$750: The 26B MoE sweet spot
RTX 4060 Ti 16GB (~$400) fits the 26B MoE at Q4 and delivers 45 tok/s — smooth for interactive chat. The RTX 5070 Ti (~$750) bumps that to 70 tok/s and has bandwidth headroom for Q5 quantization. We recommend the 5070 Ti if your budget allows it — the speed difference is significant for coding and long-form generation tasks.
$1,600+: 31B Dense territory
The RTX 4090 (~$1,600) is the entry point for the full 31B Dense model. At 28 tok/s with Q4, it is usable for interactive work. The RTX 5090 (~$2,000) is the premium choice — 32GB VRAM means room for Q5 quantization and longer context windows, plus 42 tok/s makes it genuinely fast.
See the recommended pick on the original guide
Common mistakes
- Buying a 24GB card just for the 26B MoE. The MoE variant fits on 16GB cards. Save the RTX 4090 money unless you specifically need the 31B Dense.
- Underestimating KV cache overhead. The 26B MoE weights are ~14GB at Q4, but a 4K+ context conversation pushes total usage to 16-18GB. On a 16GB card, keep context short or use Q4 with reduced context.
- Ignoring the 26B MoE entirely. Many buyers jump straight to the 31B Dense for "more parameters." The 26B MoE delivers 90%+ of the quality at a fraction of the hardware cost. Test it first.
Final verdict
| Your goal | Best GPU | Price |
|---|---|---|
| E2B / E4B only | RTX 3060 12GB (used) | ~$150 |
| 26B MoE (best value) | RTX 4060 Ti 16GB | ~$400 |
| 26B MoE (best speed) | RTX 5070 Ti | ~$750 |
| 31B Dense (budget) | RTX 3090 (used) | ~$600 |
| 31B Dense (best speed) | RTX 4090 | ~$1,600 |
| Every variant, max quality | RTX 5090 | ~$2,000 |
Our pick for most users: RTX 5070 Ti at $750. It runs the 26B MoE — the standout Gemma 4 model — at 70 tok/s with headroom. That is the best quality-to-cost ratio in the Gemma 4 lineup.
See the recommended pick on the original guide
For a detailed VRAM breakdown of every quantization level, see how much VRAM for Gemma 4. Upgrading from Gemma 3? Our Gemma 3 GPU guide covers the differences. And for the original Gemma family, check best GPU for Gemma. Budget-conscious buyers should also see our best budget GPU for local LLM roundup.
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Read the full guide on Best GPU for LLM — includes our VRAM calculator, GPU comparison table, and live pricing.
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