Can I Run Qwen3-Coder on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

作者： Jakub Rusinowski · 最后更新： 2026年9月29日

Yes, but it is tight

Yes, but it is tight — Qwen3-Coder 30B-A3B (MoE) at Q3_K_M needs about 14.6 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving only ~1.4 GB before the runtime starts swapping. Expect ~131 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q3_K_M · Estimated speed: ~131 tok/s

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RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models	16 GB
Memory bandwidth	448 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

Qwen3-Coder on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

Quant	Memory needed	Fits 16 GB?	Max context	Est. speed	Download
F16	62.6 GB	✗ No	—	—	61 GB
Q8_0	34 GB	✗ No	—	—	32.4 GB
Q6_K	26.6 GB	✗ No	—	—	25 GB
Q5_K_M	23.2 GB	✗ No	—	—	21.6 GB
Q4_K_M	20 GB	✗ No	—	—	18.4 GB
Q3_K_M	14.6 GB	✓ Yes	16K	~131 tok/s	13 GB
Q2_K	11.6 GB	✓ Yes	32K	~149.9 tok/s	10 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)	49.4 GB	✗ Too large	—
Qwen3-Coder 30B-A3B (MoE)	20 GB	✗ Too large	—

What to watch out for

Only ~1.4 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
3 larger variants of Qwen3-Coder do not fit and would need CPU offload or different hardware.

RTX 5060 Ti 16 GB desktop limitations

The cheapest current 16 GB card, but its 448 GB/s bandwidth caps generation speed well below a 5080 on the same model.

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 5060 Ti 16GB at 448 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.

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