GoogleGemma 2 9B Instruct on NVIDIA RTX 4070 Laptop (80-115W)
RTX 4070 Laptop (80-115W) runs Gemma 2 9B Instruct at Q4_K_M — 20 tok/s. Usable on 8 GB VRAM — see full quantization options below.
Model Size
9.24B
Device VRAM
8 GB
Bandwidth
256 GB/s
Quantization
Q4_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 2 9B Instruct on NVIDIA RTX 4070 Laptop (80-115W) at Q4_K_M. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q4_K_M | 20 tok/s | 343ms | ✓ Yes | Acceptable | estimated |
Notes
Q4_K_M
Sustained post-thermal-throttle performance. TDP-capped laptop variant.
About Gemma 2 9B Instruct
Gemma 2 9B Instruct (9.24B) is a chat, coding, reasoning, multi-purpose model. Google's 9B model with effective knowledge distillation. Competitive with Llama 3.1 8B on most benchmarks. Shorter max context (8K) is the main limitation vs Llama.
View all Gemma 2 9B Instruct 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: Community laptop benchmarks and thermal throttle reports. Reference hardware source: reddit.com (2026-03-14)
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.