RTX 4070 Runs Compact Gemma 4 MoE | Generated by AI
Question: Can RTX 4070 run unsloth/gemma-4-26B-A4B-it-UD-IQ1_M.gguf?
Answer:
Yes — RTX 4070 (12GB) can run it, and much better than Qwen-27B, because this is a MoE (A4B active) model with very aggressive IQ1 quantization.
But there are important details.
1. Memory requirement (Gemma-4-26B A4B)
Unsloth documentation states:
- Gemma-4-26B-A4B (4-bit) needs ~16–18 GB total memory
- If less memory → CPU offload still works but slower (Unsloth - Train and Run Models Locally)
Your RTX 4070:
- VRAM: 12GB
- System RAM (likely 32GB+) → OK
So this model fits using GPU + CPU hybrid.
2. IQ1_M version is extremely small
Other quant sizes from the same repo:
- IQ2_XXS → ~9.88 GB
- IQ2_M → ~9.97 GB
- IQ3_S → ~11.2 GB
- IQ4_XS → ~13.4 GB (Hugging Face)
Your IQ1_M is even smaller than 9–10GB, meaning:
👉 It fits fully in 12GB VRAM 👉 Can run full-GPU 👉 Much faster than 27B Q4
3. Why Gemma-4-26B works on 4070
This model is MoE (A4B):
- total parameters: 26B
- active per token: ~4B
- behaves like 4B compute, but 26B quality
So it’s very GPU friendly.
This is why many users run 26B-A4B on 16GB cards successfully. (Reddit)
4. Expected performance on RTX 4070
With IQ1_M:
- ✅ fits in VRAM
- ✅ full GPU inference possible
- ✅ fast tokens/sec
- ⚠️ quality lower (IQ1 aggressive)
Rough estimate:
- speed: ~20–40 tok/s
- VRAM: ~9–11GB
- context: 8k–32k OK
5. Better choice for RTX 4070
Best balance:
- IQ3_S (11.2GB) → best
- IQ2_M (10GB) → good
- IQ1_M → fastest but lower quality
Avoid:
- IQ4_XS (13.4GB) → may not fit
- Q4_K → too large
Final verdict
RTX 4070 (12GB) + this model:
- ✅ Yes, runs
- ✅ likely full GPU
- ✅ better than Qwen3.5-27B
- ⚠️ IQ1 quality lower
- ⭐ very good for coding agents
This model is actually one of the best 26B-class choices for 4070.
If you’re using:
- OpenClaw
- llama.cpp
- Ollama
- LM Studio
Tell me — I’ll give exact launch command.