Can You Run Gemma 4 on an RTX 4060 (8GB VRAM)?
Yes — best fit is Gemma 4 E4B at Q4_0 (4-bit) (~5GB) ✅ Comfortable, using 7GB of usable memory on a 8GB RTX 4060.
Every Gemma 4 model on this configuration
| Model | Best quant | Size | Status |
|---|---|---|---|
| Gemma 4 E2B | SFP8 (8-bit) | ~4 GB | ✅ Comfortable |
| Gemma 4 E4B | Q4_0 (4-bit) | ~5 GB | ✅ Comfortable |
| Gemma 4 12B | Q4_0 (4-bit) | ~6.7 GB | ⚠️ Tight |
| Gemma 4 26B A4B | — | — | ❌ Won't fit |
| Gemma 4 31B | — | — | ❌ Won't fit |
How we got this verdict
8 - 1 = 7GB usable VRAM (the driver and OS reserve roughly 1 GB).
E4B 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 E4B at essentially the same ~6.1GB 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 3080 (10GB VRAM) unlocks 12B at Q4_0 (4-bit) — a step up from E4B here.
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 4060 pairs its 8GB of GDDR6 with a narrow 128-bit memory bus — notably tighter than the 3060 Ti/3070's 256-bit bus from the previous generation, despite being newer. NVIDIA compensates with a larger L2 cache on the Ada architecture, but for memory-bandwidth-sensitive workloads like long-context inference, a newer generation number doesn't automatically mean faster memory.
- ⚠128-bit bus is the narrowest of any card in this checker's GPU lineup — don't assume "newer generation" means faster memory bandwidth than an older, wider-bus card.
- ⚠Low power draw (around 115W typical board power) makes it one of the easiest cards to fit into a small-form-factor, low-power always-on box, if 8GB is enough for your target model.
Try a different configuration
Best fit: Gemma 4 E4B · Q4_0 (4-bit) · ~5 GB ✅ Comfortable
FAQ
Can a RTX 4060 (8GB VRAM) run the 31B flagship model?
No — the 31B model needs at least 17.4GB usable memory even at Q4_0, and this configuration only has 7GB usable. Gemma 4 E4B is the largest model that fits here.
What's the best Gemma 4 model for a RTX 4060 (8GB VRAM)?
Gemma 4 E4B at Q4_0 (4-bit) (~5GB) is the best fit — it's the largest model that runs comfortably within the 7GB of usable memory here.
What if I have more or less memory than 8GB VRAM?
With more (e.g. RTX 3080), you can run larger models or the same model more comfortably — see the RTX 3080 page. With less (e.g. RTX 3070 Ti), the best-fitting model gets smaller or tighter — see the RTX 3070 Ti page.
Is the RTX 4060's memory bandwidth actually lower than older cards like the 3060 Ti?
Its 128-bit bus is narrower than the 3060 Ti and 3070's 256-bit bus — Ada's larger L2 cache helps offset this for many workloads, but it's a real consideration for memory-bandwidth-bound tasks like long-context local inference.
Is the RTX 4060's low power draw useful for an always-on local-AI box?
Yes — at roughly 115W typical board power, it's one of the least power-hungry cards in this lineup, making it a reasonable pick for a small, quiet, always-running setup where 8GB is enough for your target model.
Related
Gemma 4 on RTX 3070/3080
The full setup guide for this device family.
Can You Run Gemma 4 on an RTX 3070 Ti (8GB VRAM)?
NVIDIA GPU — 8GB VRAM
Can You Run Gemma 4 on an RTX 3080 (10GB VRAM)?
NVIDIA GPU — 10GB VRAM
Gemma 4 Hardware Requirements
The full memory table this checker is built on.
Full Hardware Checker
Every device type, every model, one tool.