Can I Run Qwen3-Coder on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated September 29, 2026
Yes — comfortably
Yes, comfortably — Qwen3-Coder 8B at Q8_0 needs about 10.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving ~5.6 GB spare and running at ~67.4 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~67.4 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 |
Qwen3-Coder 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 | 17.9 GB | ✗ No | — | — | 16 GB |
| Q8_0 | 10.4 GB | ✓ Yes | 32K | ~67.4 tok/s | 8.5 GB |
| Q6_K | 8.5 GB | ✓ Yes | 32K | ~82.3 tok/s | 6.6 GB |
| Q5_K_M | 7.6 GB | ✓ Yes | 64K | ~91.6 tok/s | 5.7 GB |
| Q4_K_M | 6.8 GB | ✓ Yes | 64K | ~102.6 tok/s | 4.8 GB |
| Q3_K_M | 5.4 GB | ✓ Yes | 64K | ~128.6 tok/s | 3.4 GB |
| Q2_K | 4.6 GB | ✓ Yes | 64K | ~149.5 tok/s | 2.6 GB |
Which Qwen3-Coder sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen3-Coder 480B-A35B (MoE) | 291.6 GB | ✗ Too large | — |
| Qwen3-Coder-Next (80B-A3B MoE) | 51.6 GB | ✗ Too large | — |
| Qwen3-Coder 30B-A3B (MoE) | 20 GB | ✗ Too large | — |
| Qwen3-Coder 8B | 6.8 GB | ✓ Fits | ~102.6 tok/s |
What to watch out for
- 3 larger variants of Qwen3-Coder do not fit and would need CPU offload or different hardware.
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
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 inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run Qwen3-Coder on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Yes, comfortably — Qwen3-Coder 8B at Q8_0 needs about 10.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM), leaving ~5.6 GB spare and running at ~67.4 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Qwen3-Coder should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q8_0 — it needs about 10.4 GB of the 16 GB available, downloads as roughly 8.5 GB, and runs at an estimated 67.4 tokens/sec with up to 32K of context.
What limits Qwen3-Coder 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)
- SmolLM2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- SmolLM3 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- StarCoder 2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Aya 3B (Tiny Aya) on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- VibeThinker on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
Qwen3-Coder on GPUs
- Qwen3-Coder on NVIDIA GeForce RTX 5070
- Qwen3-Coder on NVIDIA GeForce RTX 5060 Ti 8GB
- Qwen3-Coder on NVIDIA GeForce RTX 5060
- Qwen3-Coder on NVIDIA GeForce RTX 4070 Ti
What This Model Is Good At
Model & Tools
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