Can I Run IBM Granite 4.1 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated April 29, 2026

Yes

Yes — Granite 4.1 8B at Q8_0 needs about 10.4 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.6 GB spare), at ~27.9 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~27.9 tok/s

RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models12 GB
Memory bandwidth360 GB/s
Form factorDesktop
Operating systemWindows or Linux
Memory upgradeableYes

IBM Granite 4.1 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM): memory by quantization

QuantMemory neededFits 12 GB?Max contextEst. speedDownload
F1617.9 GB✗ No16 GB
Q8_010.4 GB✓ Yes16K~27.9 tok/s8.5 GB
Q6_K8.5 GB✓ Yes32K~34.9 tok/s6.6 GB
Q5_K_M7.6 GB✓ Yes32K~39.4 tok/s5.7 GB
Q4_K_M6.8 GB✓ Yes32K~44.9 tok/s4.8 GB
Q3_K_M5.4 GB✓ Yes32K~58.9 tok/s3.4 GB
Q2_K4.6 GB✓ Yes32K~70.9 tok/s2.6 GB

Which IBM Granite 4.1 sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
Granite 4.1 30B20.7 GB✗ Too large
Granite 4.1 8B6.8 GB✓ Fits~44.9 tok/s
Granite 4.1 3B3.5 GB✓ Fits~95.2 tok/s

What to watch out for

RTX 3060 12 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 IBM Granite 4.1 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

Yes — Granite 4.1 8B at Q8_0 needs about 10.4 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.6 GB spare), at ~27.9 tok/s (estimated), with room for about 16,384 tokens of context.

Which quantization of IBM Granite 4.1 should I use on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

Q8_0 — it needs about 10.4 GB of the 12 GB available, downloads as roughly 8.5 GB, and runs at an estimated 27.9 tokens/sec with up to 16K of context.

What limits IBM Granite 4.1 on RTX 3060 12 GB Desktop (12 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 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)

IBM Granite 4.1 on GPUs

What This Model Is Good At

Model & Tools

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