Can I Run Poolside Laguna XS 2.1 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

作者： Jakub Rusinowski · 最后更新： 2026年8月15日

Yes

Yes — Laguna XS 2.1 33B-A3B at Q2_K needs about 13.5 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~2.5 GB spare), at ~204.2 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~204.2 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

Poolside Laguna XS 2.1 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	68.6 GB	✗ No	—	—	66 GB
Q8_0	37.7 GB	✗ No	—	—	35.1 GB
Q6_K	29.7 GB	✗ No	—	—	27.1 GB
Q5_K_M	26 GB	✗ No	—	—	23.4 GB
Q4_K_M	22.6 GB	✗ No	—	—	19.9 GB
Q3_K_M	16.7 GB	✗ No	—	—	14.1 GB
Q2_K	13.5 GB	✓ Yes	16K	~204.2 tok/s	10.8 GB

What to watch out for

Q2_K 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.
1 larger variant of Poolside Laguna XS 2.1 does 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.

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