Can I Run / Qwen 3.6 27B / on NVIDIA RTX 6000 Ada
Can I Run Qwen 3.6 27B on a NVIDIA RTX 6000 Ada?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
27.0B
GPU memory
48.0GB
Smallest quant
Q4_K_M
Best fit
Q8_0
4 quantizations fit your 48.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 29.7 GB | 31.2 GB | 28.7 GB | +18.3 GB |
| Q6_K | 23.2 GB | 24.7 GB | 22.2 GB | +24.8 GB |
| Q5_K_M | 20.2 GB | 21.7 GB | 19.2 GB | +27.8 GB |
| Q4_K_M | 17.4 GB | 18.9 GB | 16.4 GB | +30.6 GB |
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Full model details
Qwen 3.6 27B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA RTX 6000 Ada →
Top-ranked open-source models that fit in 48.0GB.
FAQ
Can the NVIDIA RTX 6000 Ada run Qwen 3.6 27B?
Yes. The NVIDIA RTX 6000 Ada's 48.0GB of VRAM is enough to run Qwen 3.6 27B at Q8_0 quantization (29.7GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 48.0GB. It uses about 29.7GB of memory and 31.2GB recommended for comfortable inference.
What if I need more headroom for context length?
KV cache memory grows with context length. The numbers above assume a baseline 2K-4K context. For long-context use (32K+), add another 2-6GB depending on the model architecture.