GoogleGemma 4 E2B on NVIDIA RTX 4080 Laptop (120-150W)
Yes — RTX 4080 Laptop (120-150W) handles Gemma 4 E2B well at Q8_0 — 57 tok/s. Solid daily-driver performance on 12 GB VRAM.
Model Size
5.1B
Device VRAM
12 GB
Bandwidth
384 GB/s
Quantization
Q8_0
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 E2B on NVIDIA RTX 4080 Laptop (120-150W) at Q8_0. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q8_0 | 57 tok/s | 100ms | ✓ Yes | Good | estimated |
Notes
Q8_0
5.1B model. Q8_0 4.97GB on 12GB 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 RTX 4080 Laptop (120-150W)
NVIDIA RTX 4080 Laptop (120-150W) has 12 GB at 384 GB/s. Street price: $0.
See all models NVIDIA RTX 4080 Laptop (120-150W) can run →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.