Can I Run Aya 3B (Tiny Aya) on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated February 17, 2026

Yes — comfortably

Yes, comfortably — Aya 3B at Q8_0 needs about 4.8 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~11.2 GB spare and running at ~77 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~77 tok/s

RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models16 GB
Memory bandwidth448 GB/s
Form factorDesktop
Operating systemWindows or Linux
Memory upgradeableYes

Aya 3B (Tiny Aya) on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

QuantMemory neededFits 16 GB?Max contextEst. speedDownload
F167.6 GB✓ Yes8K~46.8 tok/s6 GB
Q8_04.8 GB✓ Yes8K~77 tok/s3.2 GB
Q6_K4.1 GB✓ Yes8K~92.5 tok/s2.5 GB
Q5_K_M3.8 GB✓ Yes8K~101.8 tok/s2.1 GB
Q4_K_M3.5 GB✓ Yes8K~112.6 tok/s1.8 GB
Q3_K_M2.9 GB✓ Yes8K~137 tok/s1.3 GB
Q2_K2.6 GB✓ Yes8K~155.5 tok/s1 GB

What to watch out for

RTX 5060 Ti 16 GB desktop limitations

Recommended setup

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

How these numbers are calculated

FAQ

Can I run Aya 3B (Tiny Aya) on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

Yes, comfortably — Aya 3B at Q8_0 needs about 4.8 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~11.2 GB spare and running at ~77 tok/s (estimated), with room for about 8,192 tokens of context.

Which quantization of Aya 3B (Tiny Aya) should I use on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

Q8_0 — it needs about 4.8 GB of the 16 GB available, downloads as roughly 3.2 GB, and runs at an estimated 77 tokens/sec with up to 8K of context.

What limits Aya 3B (Tiny Aya) on RTX 5060 Ti 16 GB Desktop (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 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)

Aya 3B (Tiny Aya) on GPUs

What This Model Is Good At

Model & Tools

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