Can I run Qwen 3.6 27B on an RTX 3060 12GB (12GB)?
Computed with the open FitLLM engine — accurate per-layer KV-cache modeling, not a naive estimate. Updated 2026-07-16.
Memory breakdown (Q4_K_M, F16 KV, 33K context)
| Model weights | 15.5 GB |
| KV cache | 2.0 GB |
| Runtime overhead + reserve | 5.1 GB |
| Total used | 22.6 / 12 GB |
| Short by | 10.6 GB |
Max context that fits at Q4_K_M: does not fit.
Every quantization on the RTX 3060 12GB
| Weight quant | Weights | Fits (KV F16) | Used @32K |
|---|---|---|---|
| Q4_K_M | 15.5 GB | ❌ won't fit | 22.6 / 12.0 GB |
| Q5_K_M | 18.1 GB | ❌ won't fit | 25.5 / 12.0 GB |
| Q6_K | 20.8 GB | ❌ won't fit | 28.6 / 12.0 GB |
| Q8_0 | 26.9 GB | ❌ won't fit | 35.4 / 12.0 GB |
| FP16 | 50.7 GB | ❌ won't fit | 62.0 / 12.0 GB |
Lower weight quants free memory at some output-quality cost — Q4 is the common sweet spot; below that quality drops faster.
KV cache is F16 here (llama.cpp default). Drop it to Q8/Q4 (-ctk/-ctv) for more context.
Embed this verdict
Live badge for your README or model card — recomputed by the engine, never stale:
[](https://fitllm.run/can-i-run/qwen-3-6-27b-on-rtx-3060-12gb)
Or from your terminal (exit 0/1 — works as a pre-download guard):
npx fitllm "Qwen 3.6 27B" --gpu "RTX 3060 12GB"Why most VRAM calculators get this wrong
Qwen 3.6 27B is a hybrid model: only 16 of its 64 layers use full attention — the rest are linear and keep no growing KV cache. Naive calculators count every layer at full context and badly over-estimate.
What fits on the RTX 3060 12GB instead
same GPU Models that fit on the RTX 3060 12GB: Gemma 4 e2b, Gemma 4 e4b, Gemma 4 12b, Llama-3.2-3B-Instruct, Llama-3.1-8B-Instruct, MiniCPM5-1B, Qwen3-0.6B, Qwen3-1.7B, Llama-3.2-1B-Instruct, Gemma-3-1B-it.
same model GPUs that run Qwen 3.6 27B: RTX 5090 (32GB), RTX 4090 (24GB), RTX 3090 (24GB), RTX 3090 Ti (24GB), RTX 6000 Ada (48GB), RTX PRO 6000 Blackwell (96GB), RX 7900 XTX (24GB), Radeon PRO W7900 (48GB), 2× RTX 3090 (48GB), 2× RTX 4090 (48GB), 4× RTX 3090 (96GB), A100 40GB (40GB), A100 80GB (80GB), H100 80GB (80GB), H200 141GB (141GB), B200 (180GB).
Reproduce it
Qwen 3.6 27B = 27.2B, 64 layers. The RTX 3060 12GB has 12GB / 360GB/s. Same math, open source: fitllm-engine. GGUF bpw from llama.cpp.
All numbers are computed by the open-source fitllm-engine (MIT) from official model config.json values — reproduce or audit them yourself. Estimates; real usage varies with runtime (llama.cpp / MLX / Ollama), driver and display. Found a mismatch? Report it. · FitLLM home