Can I Run Phi 3.5 Family on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

作者: Jakub Rusinowski · 最后更新: 2024年8月20日

Yes — comfortably

Yes, comfortably — Phi 3.5 Mini at Q8_0 needs about 5.6 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~10.4 GB spare and running at ~64.7 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~64.7 tok/s

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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

Phi 3.5 Family on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

QuantMemory neededFits 16 GB?Max contextEst. speedDownload
F169.2 GB✓ Yes64K~38.4 tok/s7.6 GB
Q8_05.6 GB✓ Yes64K~64.7 tok/s4 GB
Q6_K4.7 GB✓ Yes64K~78.6 tok/s3.1 GB
Q5_K_M4.3 GB✓ Yes64K~87.3 tok/s2.7 GB
Q4_K_M3.9 GB✓ Yes128K~97.3 tok/s2.3 GB
Q3_K_M3.2 GB✓ Yes128K~121 tok/s1.6 GB
Q2_K2.9 GB✓ Yes128K~139.6 tok/s1.2 GB
Superseded model. Phi 3.5 Family has been superseded by Phi-4 Family. This page is kept for reference; the newer family is a better starting point. View Phi-4 Reasoning Vision 15B →

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 Phi 3.5 Family on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

Yes, comfortably — Phi 3.5 Mini at Q8_0 needs about 5.6 GB of the 16 GB usable on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM), leaving ~10.4 GB spare and running at ~64.7 tok/s (estimated), with room for about 65,536 tokens of context.

Which quantization of Phi 3.5 Family should I use on RTX 5060 Ti 16 GB Desktop (16 GB VRAM, 32 GB RAM)?

Q8_0 — it needs about 5.6 GB of the 16 GB available, downloads as roughly 4 GB, and runs at an estimated 64.7 tokens/sec with up to 64K of context.

What limits Phi 3.5 Family 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)

Phi 3.5 Family on GPUs

What This Model Is Good At

Model & Tools

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