Can I Run Poolside Laguna XS 2.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes
Yes — Laguna XS 2.1 33B-A3B at Q2_K needs about 13.5 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2.5 GB spare), at ~172.5 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~172.5 tok/s
RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 717 GB/s |
| Form factor | Laptop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Poolside Laguna XS 2.1 on RTX 4090 Laptop (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 | ~172.5 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 4090 laptop limitations
- A mobile RTX 4090 carries 16 GB, not the desktop card's 24 GB, and is closer to a desktop 4080 in throughput — which is why it is modelled against that chip here.
- Sustained throughput depends on the chassis power limit; thin laptops throttle well below the quoted figures.
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 4080 at 717 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 Poolside Laguna XS 2.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Yes — Laguna XS 2.1 33B-A3B at Q2_K needs about 13.5 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) (~2.5 GB spare), at ~172.5 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Poolside Laguna XS 2.1 should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?
Q2_K — it needs about 13.5 GB of the 16 GB available, downloads as roughly 10.8 GB, and runs at an estimated 172.5 tokens/sec with up to 16K of context.
What limits Poolside Laguna XS 2.1 on RTX 4090 Laptop (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 4090 Laptop (16 GB VRAM, 32 GB RAM)
Poolside Laguna XS 2.1 on GPUs
What This Model Is Good At
Model & Tools
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