Qwen3.5-27B Needs More VRAM | Generated by AI
Question: Could RTX 4070 run Qwen3.5-27B.Q4_K_M.gguf?
Answer:
Short answer: No — not fully on GPU. An RTX 4070 (12GB VRAM) is too small to run Qwen3.5-27B Q4_K_M entirely in VRAM. But you can run it with CPU offloading, just slower.
1. VRAM requirement vs RTX 4070
- Qwen3.5-27B Q4_K_M weights ≈ 16 GB
- Runtime total (with KV cache) ≈ 18–20 GB VRAM
- Recommended GPU: 24GB (RTX 3090 / 4090) (LocalLLM.in)
RTX 4070 only has 12GB VRAM, which is far below that.
Typical guidance:
- 12GB GPU → best for ~14B models
- ~27B models need ~20GB VRAM (LocalLLM.in)
So 4070 cannot fully load Qwen3.5-27B Q4_K_M.
2. What actually happens on RTX 4070
You still can run it, but:
- Some layers offloaded to CPU RAM
- Very slow generation
- High system RAM usage (32GB+ recommended)
- GPU only partially used
Even the model repo notes:
- 12GB GPU → partial only, use GPU layer offloading
- 24GB GPU → comfortable full-GPU run (Hugging Face)
3. Realistic expectations on RTX 4070
You can do:
- llama.cpp with
--n-gpu-layers - LM Studio CPU+GPU hybrid
- Ollama partial offload
But expect:
- ~1–4 tokens/sec
- high latency
- not good for agent coding
4. What runs well on RTX 4070 (12GB)
Good choices:
- Qwen3.5-14B Q4
- DeepSeek-R1 8B / 14B
- Qwen2.5-14B
- Llama-3-8B fast
- Qwen3-9B (best fit)
These run fully on GPU.
5. If you really want 27B on 4070
Use one of these:
- Q3_K_M quant (smaller)
- IQ3_M quant
- GPU + CPU offload
- small context (2k–4k)
- KV cache quantization
Bottom line
RTX 4070 (12GB):
- ❌ Qwen3.5-27B Q4_K_M full GPU → No
- ⚠️ hybrid CPU+GPU → Yes but slow
- ✅ best size → ≤14B models
If you tell me:
- RAM size (32GB? 64GB?)
- llama.cpp / ollama / openclaw
- target usage (agent / coding / chat)
I’ll tell you best model for your 4070.