Can I Run Qwen 3.5 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated February 24, 2026
Yes — comfortably
Yes, comfortably — Qwen 3.5 9B at Q8_0 needs about 10.6 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving ~5.4 GB spare and running at ~63.6 tok/s (estimated), with room for about 131,072 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~63.6 tok/s
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RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 960 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Qwen 3.5 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 19.1 GB | ✗ No | — | — | 18 GB |
| Q8_0 | 10.6 GB | ✓ Yes | 128K | ~63.6 tok/s | 9.6 GB |
| Q6_K | 8.4 GB | ✓ Yes | 128K | ~78.8 tok/s | 7.4 GB |
| Q5_K_M | 7.4 GB | ✓ Yes | 128K | ~88.6 tok/s | 6.4 GB |
| Q4_K_M | 6.5 GB | ✓ Yes | 128K | ~100.2 tok/s | 5.4 GB |
| Q3_K_M | 4.9 GB | ✓ Yes | 128K | ~128.9 tok/s | 3.8 GB |
| Q2_K | 4 GB | ✓ Yes | 128K | ~152.9 tok/s | 3 GB |
Which Qwen 3.5 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen 3.5 397B-A17B | 244.7 GB | ✗ Too large | — |
| Qwen 3.5 122B-A10B | 77.3 GB | ✗ Too large | — |
| Qwen 3.5 35B-A3B | 23.8 GB | ✗ Too large | — |
| Qwen 3.5 27B | 18.8 GB | ✗ Too large | — |
| Qwen 3.5 9B | 6.5 GB | ✓ Fits | ~100.2 tok/s |
| Qwen 3.5 4B | 3.5 GB | ✓ Fits | ~172.8 tok/s |
| Qwen 3.5 2B | 2.7 GB | ✓ Fits | ~225.4 tok/s |
| Qwen 3.5 0.8B | 1.8 GB | ✓ Fits | ~304 tok/s |
What to watch out for
- 4 larger variants of Qwen 3.5 do not fit and would need CPU offload or different hardware.
RTX 5080 desktop limitations
- 16 GB VRAM is the binding constraint, not compute — a slower 24 GB card runs strictly more models.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 16 GB of VRAM on the NVIDIA GeForce RTX 5080 at 960 GB/s.
- 32 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run Qwen 3.5 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Yes, comfortably — Qwen 3.5 9B at Q8_0 needs about 10.6 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving ~5.4 GB spare and running at ~63.6 tok/s (estimated), with room for about 131,072 tokens of context.
Which quantization of Qwen 3.5 should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 10.6 GB of the 16 GB available, downloads as roughly 9.6 GB, and runs at an estimated 63.6 tokens/sec with up to 128K of context.
What limits Qwen 3.5 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Qwen 3.6 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Qwen 3.7 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Qwen3.8 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Qwen3-Coder on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- SmolLM2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
Qwen 3.5 on GPUs
- Qwen 3.5 on NVIDIA GeForce RTX 5090
- Qwen 3.5 on NVIDIA GeForce RTX 5070
- Qwen 3.5 on NVIDIA GeForce RTX 5060 Ti 8GB
- Qwen 3.5 on NVIDIA GeForce RTX 5060