Can I Run / Qwen3.5-27B / on NVIDIA RTX 5090
Can I Run Qwen3.5-27B on a NVIDIA RTX 5090?
Yes
Runs at Q8_0 — near-lossless quality.
Model size
27.8B
GPU memory
32.0GB
Smallest quant
Q3_K_S
Best fit
Q8_0
10 quantizations fit your 32.0GB
| Quant | Min VRAM | Recommended | File size | Headroom |
|---|---|---|---|---|
| Q8_0BEST | 30.5 GB | 32.0 GB | 28.6 GB | +1.5 GB |
| Q6_K | 23.9 GB | 25.4 GB | 22.4 GB | +8.1 GB |
| Q5_K_M | 20.7 GB | 22.2 GB | 19.6 GB | +11.3 GB |
| Q5_K_S | 20.2 GB | 21.7 GB | 18.9 GB | +11.8 GB |
| Q4_1 | 18.4 GB | 19.9 GB | 17.2 GB | +13.6 GB |
| Q4_K_M | 17.9 GB | 19.4 GB | 16.7 GB | +14.1 GB |
| Q4_K_S | 16.9 GB | 18.4 GB | 15.8 GB | +15.1 GB |
| Q4_0 | 16.6 GB | 18.1 GB | 15.7 GB | +15.4 GB |
| Q3_K_M | 12.6 GB | 14.1 GB | 13.5 GB | +19.4 GB |
| Q3_K_S | 11.7 GB | 13.2 GB | 12.3 GB | +20.3 GB |
Try it in the cloud first
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Full model details
Qwen3.5-27B →
All quant variants, benchmark scores, and use-case tags.
Best models for this GPU
NVIDIA RTX 5090 →
Top-ranked open-source models that fit in 32.0GB.
FAQ
Can the NVIDIA RTX 5090 run Qwen3.5-27B?
Yes. The NVIDIA RTX 5090's 32.0GB of VRAM is enough to run Qwen3.5-27B at Q8_0 quantization (30.5GB required).
What's the best quantization to use?
Q8_0 is the highest-precision quantization that fits in your 32.0GB. It uses about 30.5GB of memory and 32.0GB 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.