GoogleGemma 4 26B-A4B on NVIDIA GeForce RTX 4060 Ti 16GB
Yes — RTX 4060 Ti 16GB runs Gemma 4 26B-A4B excellently at Q3_K_M — 98 tok/s. 16 GB VRAM with plenty of headroom.
Model Size
25.2B
Device VRAM
16 GB
Bandwidth
288 GB/s
Quantization
Q3_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 4 26B-A4B on NVIDIA GeForce RTX 4060 Ti 16GB 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 | 98 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 GeForce RTX 4060 Ti 16GB
NVIDIA GeForce RTX 4060 Ti 16GB has 16 GB at 288 GB/s. Street price: $449.
See all models NVIDIA GeForce RTX 4060 Ti 16GB can run →Builds with NVIDIA GeForce RTX 4060 Ti 16GB
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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.