Can You Run Gemma 4 on an RTX 3090 (24GB VRAM)?
Yes — best fit is Gemma 4 31B at Q4_0 (4-bit) (~17.4GB) ✅ Comfortable, using 23GB of usable memory on a 24GB RTX 3090.
Every Gemma 4 model on this configuration
| Model | Best quant | Size | Status |
|---|---|---|---|
| Gemma 4 E2B | BF16 (16-bit) | ~8 GB | ✅ Comfortable |
| Gemma 4 E4B | BF16 (16-bit) | ~15 GB | ✅ Comfortable |
| Gemma 4 12B | SFP8 (8-bit) | ~13.4 GB | ✅ Comfortable |
| Gemma 4 26B A4B | Q4_0 (4-bit) | ~15.6 GB | ✅ Comfortable |
| Gemma 4 31B | Q4_0 (4-bit) | ~17.4 GB | ✅ Comfortable |
How we got this verdict
24 - 1 = 23GB usable VRAM (the driver and OS reserve roughly 1 GB).
31B lands on Q4_0 here, so grab the official QAT (quantization-aware training) checkpoint instead of a generic Q4_0 quant. Google shipped a full-quality QAT build for 31B at essentially the same ~19GB download size, with noticeably better output quality than a naive 4-bit quant. See the QAT guide for exact file names.
Switching to the RTX 4090 (24GB VRAM) doesn't change the top recommendation — 31B is still the best fit, just with more headroom. One tier down, at 16GB VRAM, the best fit drops to 12B (comfortable) instead.
The fastest lever on a fixed budget isn't more VRAM on the same card — it's switching cards. Compare this tier against neighboring cards below, or check the full checker for any GPU model.
Real-world notes for this configuration
The RTX 3090 was the last consumer NVIDIA card to support NVLink, letting two cards bridge together for workloads written to use it — a feature dropped entirely from the 40-series onward. Its 24GB of GDDR6X, combined with a now-mature, discounted used market, makes it one of the more cost-effective ways to get 24GB of VRAM for local AI.
- ⚠Large, often 3-slot cards with 350W+ power draw — verify case clearance and PSU headroom before buying, especially for reference or blower-style variants that run hot under sustained load.
- ⚠Used pricing and condition vary enormously, including mining history — ask for a stress-test result or GPU-Z screenshot before buying secondhand specifically for AI workloads.
Try a different configuration
Best fit: Gemma 4 31B · Q4_0 (4-bit) · ~17.4 GB ✅ Comfortable
FAQ
Can a RTX 3090 (24GB VRAM) run the 31B flagship model?
Yes, comfortably — Gemma 4 31B fits at Q4_0 (4-bit) (~17.4GB), using 23GB of usable memory on this configuration.
What's the best Gemma 4 model for a RTX 3090 (24GB VRAM)?
Gemma 4 31B at Q4_0 (4-bit) (~17.4GB) is the best fit — it's the largest model that runs comfortably within the 23GB of usable memory here.
What if I have more or less memory than 24GB VRAM?
With more (e.g. RTX 4090), you can run larger models or the same model more comfortably — see the RTX 4090 page. With less (e.g. RTX 5080), the best-fitting model gets smaller or tighter — see the RTX 5080 page.
Is the RTX 3090 still a good buy for local AI in the used market?
For pure VRAM-per-dollar, it's frequently one of the better used options — 24GB was flagship-tier when new, and used prices have dropped well below the 4090's, which matters more for local AI capacity than the compute-speed gap between the two generations for many workloads.
Does the RTX 3090's NVLink help with running larger local AI models?
It helps in workloads and runtimes explicitly written to use it for tensor-parallel splitting, but most consumer local-AI tools (Ollama, LM Studio) don't use NVLink for pooling — for those, VRAM is used per-card, not combined.
Related
Gemma 4 on RTX 3070/3080
The full setup guide for this device family.
Can You Run Gemma 4 on an RTX 5080 (16GB VRAM)?
NVIDIA GPU — 16GB VRAM
Can You Run Gemma 4 on an RTX 4090 (24GB VRAM)?
NVIDIA GPU — 24GB VRAM
Gemma 4 Hardware Requirements
The full memory table this checker is built on.
Full Hardware Checker
Every device type, every model, one tool.