Can I Run Yi 1.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

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Written by Jakub Rusinowski · Last updated May 13, 2024

Yes, but it is tight

Yes, but it is tight — Yi 1.5 9B Chat at Q5_K_M needs about 7.9 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.1 GB before the runtime starts swapping. Expect ~28.7 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q5_K_M · Estimated speed: ~28.7 tok/s

RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model

Usable memory for models8 GB
Memory bandwidth272 GB/s
Form factorLaptop
Operating systemWindows or Linux
Memory upgradeableYes
Price$1,099 (lib/data/laptops.ts (street price), checked 2026-07-06)

Yi 1.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization

QuantMemory neededFits 8 GB?Max contextEst. speedDownload
F1619.3 GB✗ No17.7 GB
Q8_011 GB✗ No9.4 GB
Q6_K8.8 GB✗ No7.2 GB
Q5_K_M7.9 GB✓ Yes8K~28.7 tok/s6.3 GB
Q4_K_M6.9 GB✓ Yes16K~32.9 tok/s5.3 GB
Q3_K_M5.4 GB✓ Yes16K~44.1 tok/s3.8 GB
Q2_K4.5 GB✓ Yes16K~54.2 tok/s2.9 GB

Which Yi 1.5 Family sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
Yi 1.5 34B Chat23.6 GB✗ Too large
Yi 1.5 9B Chat6.9 GB✓ Fits~32.9 tok/s

What to watch out for

RTX 4060 laptop limitations

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

FAQ

Can I run Yi 1.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Yes, but it is tight — Yi 1.5 9B Chat at Q5_K_M needs about 7.9 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.1 GB before the runtime starts swapping. Expect ~28.7 tok/s (estimated), with room for about 8,192 tokens of context.

Which quantization of Yi 1.5 Family should I use on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Q5_K_M — it needs about 7.9 GB of the 8 GB available, downloads as roughly 6.3 GB, and runs at an estimated 28.7 tokens/sec with up to 8K of context.

What limits Yi 1.5 Family on RTX 4060 Laptop (8 GB VRAM, 16 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 4060 Laptop (8 GB VRAM, 16 GB RAM)

Yi 1.5 Family on GPUs

What This Model Is Good At

Model & Tools

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