Can I Run EXAONE 3.5 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?

Written by Jakub Rusinowski · Last updated February 10, 2026

Yes, but it is tight

Yes, but it is tight — EXAONE 3.5 32B at Q6_K needs about 29.2 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving only ~2.8 GB before the runtime starts swapping. Expect ~44.3 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~44.3 tok/s

RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) — what it gives a model

Usable memory for models32 GB
Memory bandwidth1792 GB/s
Form factorDesktop
Operating systemWindows or Linux
Memory upgradeableYes

EXAONE 3.5 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM): memory by quantization

QuantMemory neededFits 32 GB?Max contextEst. speedDownload
F1666.9 GB✗ No64 GB
Q8_036.9 GB✗ No34 GB
Q6_K29.2 GB✓ Yes16K~44.3 tok/s26.2 GB
Q5_K_M25.6 GB✓ Yes16K~50.2 tok/s22.7 GB
Q4_K_M22.3 GB✓ Yes32K~57.4 tok/s19.3 GB
Q3_K_M16.6 GB✓ Yes32K~75.8 tok/s13.6 GB
Q2_K13.5 GB✓ Yes32K~92 tok/s10.5 GB

Which EXAONE 3.5 sizes fit

VariantNeeds at Q4_K_MFits?Est. speed
EXAONE 3.5 32B22.3 GB✓ Fits~57.4 tok/s
EXAONE 3.5 7.8B6.6 GB✓ Fits~162.6 tok/s
EXAONE 3.5 2.4B2.9 GB✓ Fits~281 tok/s

RTX 5090 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 EXAONE 3.5 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?

Yes, but it is tight — EXAONE 3.5 32B at Q6_K needs about 29.2 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving only ~2.8 GB before the runtime starts swapping. Expect ~44.3 tok/s (estimated), with room for about 16,384 tokens of context.

Which quantization of EXAONE 3.5 should I use on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?

Q6_K — it needs about 29.2 GB of the 32 GB available, downloads as roughly 26.2 GB, and runs at an estimated 44.3 tokens/sec with up to 16K of context.

What limits EXAONE 3.5 on RTX 5090 Desktop (32 GB VRAM, 64 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 5090 Desktop (32 GB VRAM, 64 GB RAM)

EXAONE 3.5 on GPUs

What This Model Is Good At

Model & Tools

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