Can I Run Qwen 3 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated April 28, 2025
Yes
Yes — Qwen 3 14B at Q6_K needs about 14.3 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~1.7 GB spare), at ~49.9 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~49.9 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 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 | 31.7 GB | ✗ No | — | — | 29.6 GB |
| Q8_0 | 17.9 GB | ✗ No | — | — | 15.7 GB |
| Q6_K | 14.3 GB | ✓ Yes | 16K | ~49.9 tok/s | 12.1 GB |
| Q5_K_M | 12.6 GB | ✓ Yes | 16K | ~56.3 tok/s | 10.5 GB |
| Q4_K_M | 11.1 GB | ✓ Yes | 32K | ~64.1 tok/s | 8.9 GB |
| Q3_K_M | 8.5 GB | ✓ Yes | 32K | ~83.8 tok/s | 6.3 GB |
| Q2_K | 7 GB | ✓ Yes | 32K | ~100.6 tok/s | 4.9 GB |
Which Qwen 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen 3 235B-A22B (MoE) | 144.3 GB | ✗ Too large | — |
| Qwen 3 32B | 22.8 GB | ✗ Too large | — |
| Qwen 3 30B-A3B (MoE) | 20 GB | ✗ Too large | — |
| Qwen 3 14B | 11.1 GB | ✓ Fits | ~64.1 tok/s |
| Qwen 3 8B | 7 GB | ✓ Fits | ~100.4 tok/s |
What to watch out for
- 3 larger variants of Qwen 3 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 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Yes — Qwen 3 14B at Q6_K needs about 14.3 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~1.7 GB spare), at ~49.9 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Qwen 3 should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q6_K — it needs about 14.3 GB of the 16 GB available, downloads as roughly 12.1 GB, and runs at an estimated 49.9 tokens/sec with up to 16K of context.
What limits Qwen 3 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 3.5 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)
Qwen 3 on GPUs
- Qwen 3 on NVIDIA GeForce RTX 5090
- Qwen 3 on NVIDIA GeForce RTX 5080
- Qwen 3 on NVIDIA GeForce RTX 5070 Ti
- Qwen 3 on NVIDIA GeForce RTX 5070