GoogleGemma 4 E4B on NVIDIA RTX 4070 Laptop (80-115W)
Yes — RTX 4070 Laptop (80-115W) handles Gemma 4 E4B well at Q6_K — 30 tok/s. Solid daily-driver performance on 8 GB VRAM.
Model Size
8B
Device VRAM
8 GB
Bandwidth
256 GB/s
Quantization
Q6_K
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 E4B on NVIDIA RTX 4070 Laptop (80-115W) at Q6_K. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q6_K | 30 tok/s | 200ms | ✓ Yes | Good | estimated |
Notes
Q6_K
8.0B model. Q6_K 6.33GB on 8GB discrete_gpu.
About Gemma 4 E4B
Gemma 4 E4B (8B) is a chat, coding, reasoning model. Gemma 4's mid-range edge model. 8B total parameters with 4.5B effective. Full multimodal: text, image, audio, and video. 52% LiveCodeBench v6 and 42.5% AIME 2026 put its reasoning and coding above most models at this size. Fits comfortably on 8 GB GPUs at Q4_K_M. Apache 2.0 licensed.
View all Gemma 4 E4B hardware options →About NVIDIA RTX 4070 Laptop (80-115W)
NVIDIA RTX 4070 Laptop (80-115W) has 8 GB at 256 GB/s. Street price: $0.
See all models NVIDIA RTX 4070 Laptop (80-115W) 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.