Can I Run Llama 3.2 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated August 15, 2026

Yes

Yes — Llama 3.2 11B Vision Instruct at Q8_0 needs about 13.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~2.6 GB spare), at ~53.1 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~53.1 tok/s

RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models16 GB
Memory bandwidth960 GB/s
Form factorDesktop
Operating systemWindows or Linux
Memory upgradeableYes

Llama 3.2 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

QuantMemory neededFits 16 GB?Max contextEst. speedDownload
F1623.3 GB✗ No21.2 GB
Q8_013.4 GB✓ Yes16K~53.1 tok/s11.3 GB
Q6_K10.8 GB✓ Yes32K~65.6 tok/s8.7 GB
Q5_K_M9.7 GB✓ Yes32K~73.5 tok/s7.5 GB
Q4_K_M8.5 GB✓ Yes32K~82.9 tok/s6.4 GB
Q3_K_M6.7 GB✓ Yes32K~105.8 tok/s4.5 GB
Q2_K5.6 GB✓ Yes64K~124.7 tok/s3.5 GB

Which Llama 3.2 Family sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
Llama 3.2 90B Vision Instruct57.8 GB✗ Too large
Llama 3.2 11B Vision Instruct8.5 GB✓ Fits~82.9 tok/s
Llama 3.2 3B Instruct3.7 GB✓ Fits~178.9 tok/s
Llama 3.2 1B Instruct1.8 GB✓ Fits~288 tok/s

What to watch out for

RTX 5080 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 Llama 3.2 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Yes — Llama 3.2 11B Vision Instruct at Q8_0 needs about 13.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~2.6 GB spare), at ~53.1 tok/s (estimated), with room for about 16,384 tokens of context.

Which quantization of Llama 3.2 Family should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Q8_0 — it needs about 13.4 GB of the 16 GB available, downloads as roughly 11.3 GB, and runs at an estimated 53.1 tokens/sec with up to 16K of context.

What limits Llama 3.2 Family on RTX 5080 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 5080 Desktop (16 GB VRAM, 32 GB RAM)

Llama 3.2 Family on GPUs

What This Model Is Good At

Model & Tools

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