Can I Run EXAONE 3.5 on NVIDIA GeForce RTX 3090?
Written by Jakub Rusinowski · Last updated July 12, 2026
Yes, but it's tight — EXAONE 3.5 32B at Q4_K_M needs 22.3 GB at 8K context (19.3 GB weights + 3 GB KV cache/overhead), leaving ~1.7 GB of the NVIDIA GeForce RTX 3090's 24 GB, at ~27 tok/s (est.), with room for up to 8K context.
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NVIDIA GeForce RTX 3090 Specs
| VRAM | 24 GB |
| Memory Bandwidth | 936 GB/s |
EXAONE 3.5 32B on the NVIDIA GeForce RTX 3090: VRAM by quantization
| Quant | VRAM needed | Fits 24 GB? | Max context | Whole PC |
|---|---|---|---|---|
| F16 | 66.9 GB | ✗ No | — | — |
| Q8_0 | 36.9 GB | ✗ No | — | Build for this |
| Q6_K | 29.2 GB | ✗ No | — | — |
| Q5_K_M | 25.6 GB | ✗ No | — | Build for this |
| Q4_K_M | 22.3 GB | ✓ Yes | 8K | Build for this |
| Q3_K_M | 16.6 GB | ✓ Yes | 32K | — |
| Q2_K | 13.5 GB | ✓ Yes | 32K | — |
Assumes an 8K-token context with an f16 KV cache. A longer window needs more; a quantized KV cache needs less. “Max context” is the largest window that still fits in 24 GB. Figures are estimates from parameter count, quantization and memory bandwidth — the analyzer lets you tune KV-cache quant and context.
Context costs VRAM too. At Q4_K_M on the NVIDIA GeForce RTX 3090: 8K 22.3 GB · 32K 28.7 GB ✗. Past 32K it no longer fits 24 GB — a q8_0 KV cache buys roughly half of that back, which the calculator will price for you.
No compatible GPU? EXAONE 3.5 32B on 32 GB of system RAM, CPU only: runs comfortably.
EXAONE 3.5 Sizes That Fit the NVIDIA GeForce RTX 3090 (at 8K context)
| EXAONE 3.5 32B | Q4_K_M · 22.3 GB at 8K context · ~27 tok/s (est.) |
| EXAONE 3.5 7.8B | Q4_K_M · 6.6 GB at 8K context · ~89 tok/s (est.) |
| EXAONE 3.5 2.4B | Q4_K_M · 2.9 GB at 8K context · ~190 tok/s (est.) |
At 2 hrs/day, buying (~$1,499) beats renting at $0.34/hr after about 6.1 years.
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Cloud rates verified 2026-07 — estimates, and marketplace prices vary. Buying price is GPU MSRP only, not a full PC.
FAQ
EXAONE 3.5 on the NVIDIA GeForce RTX 3090 — does it fit?
Yes, but it's tight — EXAONE 3.5 32B at Q4_K_M needs 22.3 GB at 8K context (19.3 GB weights + 3 GB KV cache/overhead), leaving ~1.7 GB of the NVIDIA GeForce RTX 3090's 24 GB, at ~27 tok/s (est.), with room for up to 8K context.
Which EXAONE 3.5 variant fits best on the NVIDIA GeForce RTX 3090?
EXAONE 3.5 32B at Q4_K_M quantization needs 22.3 GB at 8K context (19.3 GB weights + 3 GB KV cache/overhead), estimated ~27 tokens/sec, up to 8K context.
Every EXAONE 3.5 size on the NVIDIA GeForce RTX 3090
| Size | VRAM at 8K | Verdict | Speed |
|---|---|---|---|
| EXAONE 3.5 32B | 22.3 GB | Runs | ~27 tok/s |
| EXAONE 3.5 7.8B | 6.6 GB | Runs | ~89 tok/s |
| EXAONE 3.5 2.4B | 2.9 GB | Runs | ~190 tok/s |
EXAONE 3.5 on Other GPUs
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VRAM Tier
Troubleshooting
- CUDA out of memory — why it happens and how to fix it
- Which GGUF quant should I download? (Q4 vs Q5 vs Q8)
Buying Guide
← Can I Run It? | EXAONE 3.5 Model Page | NVIDIA GeForce RTX 3090 GPU Page | VRAM calculator | Check Your Hardware