Can I Run IBM Granite 4.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated April 29, 2026

Yes — comfortably

Yes, comfortably — Granite 4.1 8B at Q8_0 needs about 10.4 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~5.6 GB spare and running at ~52.3 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~52.3 tok/s

RTX 4090 Laptop (16 GB VRAM, 32 GB RAM) — what it gives a model

Usable memory for models16 GB
Memory bandwidth717 GB/s
Form factorLaptop
Operating systemWindows or Linux
Memory upgradeableYes

IBM Granite 4.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM): memory by quantization

QuantMemory neededFits 16 GB?Max contextEst. speedDownload
F1617.9 GB✗ No16 GB
Q8_010.4 GB✓ Yes32K~52.3 tok/s8.5 GB
Q6_K8.5 GB✓ Yes32K~64.5 tok/s6.6 GB
Q5_K_M7.6 GB✓ Yes64K~72.2 tok/s5.7 GB
Q4_K_M6.8 GB✓ Yes64K~81.4 tok/s4.8 GB
Q3_K_M5.4 GB✓ Yes64K~103.6 tok/s3.4 GB
Q2_K4.6 GB✓ Yes64K~122 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~81.4 tok/s
Granite 4.1 3B3.5 GB✓ Fits~156.4 tok/s

What to watch out for

RTX 4090 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 IBM Granite 4.1 on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Yes, comfortably — Granite 4.1 8B at Q8_0 needs about 10.4 GB of the 16 GB usable on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM), leaving ~5.6 GB spare and running at ~52.3 tok/s (estimated), with room for about 32,768 tokens of context.

Which quantization of IBM Granite 4.1 should I use on RTX 4090 Laptop (16 GB VRAM, 32 GB RAM)?

Q8_0 — it needs about 10.4 GB of the 16 GB available, downloads as roughly 8.5 GB, and runs at an estimated 52.3 tokens/sec with up to 32K of context.

What limits IBM Granite 4.1 on RTX 4090 Laptop (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 4090 Laptop (16 GB VRAM, 32 GB RAM)

IBM Granite 4.1 on GPUs

What This Model Is Good At

Model & Tools

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