Can I Run North Mini Code on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026

Yes, but it is tight

Yes, but it is tight — North Mini Code 1.0 30B-A3B at Q3_K_M needs about 15.4 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving only ~0.6 GB before the runtime starts swapping. Expect ~159.3 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: low · Recommended quantization: Q3_K_M · Estimated speed: ~159.3 tok/s

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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

North Mini Code 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	62.6 GB	✗ No	—	—	60 GB
Q8_0	34.5 GB	✗ No	—	—	31.9 GB
Q6_K	27.2 GB	✗ No	—	—	24.6 GB
Q5_K_M	23.8 GB	✗ No	—	—	21.3 GB
Q4_K_M	20.7 GB	✗ No	—	—	18.1 GB
Q3_K_M	15.4 GB	✓ Yes	8K	~159.3 tok/s	12.8 GB
Q2_K	12.4 GB	✓ Yes	16K	~174.4 tok/s	9.9 GB

What to watch out for

Only ~0.6 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.
1 larger variant of North Mini Code 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.

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