GoogleGemma 4 E2B on NVIDIA GeForce RTX 3090
Yes — RTX 3090 runs Gemma 4 E2B excellently at Q8_0 — 141 tok/s. 24 GB VRAM with plenty of headroom.
Model Size
5.1B
Device VRAM
24 GB
Bandwidth
936 GB/s
Quantization
Q8_0
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 E2B on NVIDIA GeForce RTX 3090 at Q8_0. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q8_0 | 141 tok/s | 50ms | ✓ Yes | Excellent | estimated |
Notes
Q8_0
5.1B model. Q8_0 4.97GB on 24GB discrete_gpu.
About Gemma 4 E2B
Gemma 4 E2B (5.1B) is a chat, coding model. Gemma 4's compact edge model. 5.1B total parameters with 2.3B effective via Per-Layer Embeddings. Supports text, image, audio, and video input. Runs on practically any dedicated GPU with 4 GB of VRAM or more. Successor to Gemma 3 4B with measurably better reasoning and multimodal capabilities. Apache 2.0 licensed.
View all Gemma 4 E2B hardware options →About NVIDIA GeForce RTX 3090
NVIDIA GeForce RTX 3090 has 24 GB at 936 GB/s. Street price: $899.
See all models NVIDIA GeForce RTX 3090 can run →Builds with NVIDIA GeForce RTX 3090
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Estimate method: Estimated from model architecture, quantization size, and device bandwidth. Reference hardware source: huggingface.co (2026-04-04)
Performance varies by driver version, inference engine, quantization method, context length, and system configuration. Figures shown are estimates based on community benchmarks and may not reflect your exact setup. Product names are trademarks of their respective owners. OwnRig is independent and not affiliated with any hardware or AI model provider.