Can I Run Aya Expanse on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated October 8, 2024

Yes — comfortably

Yes, comfortably — Aya Expanse 8B at Q8_0 needs about 10.4 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~5.6 GB spare and running at ~43 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~43 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	576 GB/s
Form factor	Laptop
Operating system	Windows or Linux
Memory upgradeable	Yes

Aya Expanse 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	17.9 GB	✗ No	—	—	16.1 GB
Q8_0	10.4 GB	✓ Yes	8K	~43 tok/s	8.5 GB
Q6_K	8.5 GB	✓ Yes	8K	~53.4 tok/s	6.6 GB
Q5_K_M	7.6 GB	✓ Yes	8K	~60 tok/s	5.7 GB
Q4_K_M	6.7 GB	✓ Yes	8K	~68 tok/s	4.8 GB
Q3_K_M	5.3 GB	✓ Yes	8K	~87.6 tok/s	3.4 GB
Q2_K	4.5 GB	✓ Yes	8K	~104.2 tok/s	2.6 GB

Which Aya Expanse sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Aya Expanse 32B	21.6 GB	✗ Too large	—
Aya Expanse 8B	6.7 GB	✓ Fits	~68 tok/s

What to watch out for

1 larger variant of Aya Expanse does not fit and would need CPU offload or different hardware.

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 4090 Laptop GPU at 576 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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