Can I Run Yi 1.5 Family on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

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Written by Jakub Rusinowski · Last updated May 13, 2024

Yes — comfortably

Yes, comfortably — Yi 1.5 9B Chat at Q8_0 needs about 11 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~5 GB spare and running at ~48.9 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~48.9 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

Yi 1.5 Family 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	19.3 GB	✗ No	—	—	17.7 GB
Q8_0	11 GB	✓ Yes	16K	~48.9 tok/s	9.4 GB
Q6_K	8.8 GB	✓ Yes	16K	~60.8 tok/s	7.2 GB
Q5_K_M	7.9 GB	✓ Yes	16K	~68.3 tok/s	6.3 GB
Q4_K_M	6.9 GB	✓ Yes	16K	~77.5 tok/s	5.3 GB
Q3_K_M	5.4 GB	✓ Yes	16K	~100.1 tok/s	3.8 GB
Q2_K	4.5 GB	✓ Yes	16K	~119.1 tok/s	2.9 GB

Which Yi 1.5 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Yi 1.5 34B Chat	23.6 GB	✗ Too large	—
Yi 1.5 9B Chat	6.9 GB	✓ Fits	~77.5 tok/s

What to watch out for

1 larger variant of Yi 1.5 Family 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 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 computed from this model's published attention configuration.

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