GoogleGemma 4 26B-A4B on NVIDIA RTX 4090 Laptop (150-175W)
Yes — RTX 4090 Laptop (150-175W) runs Gemma 4 26B-A4B excellently at Q3_K_M — 175 tok/s. 16 GB VRAM with plenty of headroom.
Model Size
25.2B
Device VRAM
16 GB
Bandwidth
512 GB/s
Quantization
Q3_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 26B-A4B on NVIDIA RTX 4090 Laptop (150-175W) at Q3_K_M. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q3_K_M | 175 tok/s | 50ms | ✓ Yes | Excellent | estimated |
Notes
Q3_K_M
25.2B model. Q3_K_M 12.36GB on 16GB discrete_gpu.
About Gemma 4 26B-A4B
Gemma 4 26B-A4B (25.2B) is a chat, coding, reasoning, multi-purpose model. Mixture-of-Experts architecture: 25.2B total parameters but only 3.8B active per token (8 selected + 1 shared expert per layer, out of 128 total). Hybrid dense+sparse FFN design. Inference throughput closer to a 4B dense model; quality closer to a 27B dense model. 256K context window. Benchmarks: 88.3% AIME 2026, 82.6% MMLU Pro, 77.1% LiveCodeBench. All 25.2B weights must be loaded into VRAM despite sparse activation; fits on 24 GB GPUs at Q4_K_M. Apache 2.0 licensed.
View all Gemma 4 26B-A4B hardware options →About NVIDIA RTX 4090 Laptop (150-175W)
NVIDIA RTX 4090 Laptop (150-175W) has 16 GB at 512 GB/s. Street price: $0.
See all models NVIDIA RTX 4090 Laptop (150-175W) can run →Estimate method: Estimated from MoE active params (3.8B), quantization, and device bandwidth with 0.65 efficiency factor. 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.