EXAONE 4.0 32B needs ~25.7 GB but RTX 4000 Ada Laptop 12GB only has 12.0 GB. Try a smaller quantization or lighter model.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
13.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.4 tok/s
TTFT
79192 ms
Safe context
4K
Memory
25.7 GB / 12.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 25.7 GB, but this setup only exposes 12.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.9 tok/s | 36822 ms | 4K |
| Coding | F | Too heavy | 2.4 tok/s | 79192 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 116202 ms | 4K |
| Reasoning | F | Too heavy | 2.4 tok/s | 93591 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 145253 ms | 4K |
How EXAONE 4.0 32B (32B params) fits at each quantization level on RTX 4000 Ada Laptop 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | F0 |
Q3_K_S | 3 | 15.7 GB | Low | F0 |
NVFP4 | 4 | 17.9 GB | Medium | F0 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Opciones de mejora
RTX 3090 24GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 725%.
~$1,499 MSRP
RTX 4090 24GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 867%.
~$1,599 MSRP
RTX 5090 32GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,999 MSRP
No, EXAONE 4.0 32B requires more memory than RTX 4000 Ada Laptop 12GB provides.
EXAONE 4.0 32B (32B parameters) requires approximately 25.7 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4000 Ada Laptop 12GB, EXAONE 4.0 32B achieves approximately 2.4 tokens per second decode speed with a time-to-first-token of 79192ms using Q4_K_M quantization.
For coding workloads, EXAONE 4.0 32B on RTX 4000 Ada Laptop 12GB receives a F grade with 2.4 tok/s and 4K context.
On RTX 4000 Ada Laptop 12GB, EXAONE 4.0 32B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-lgai-exaone--exaone-4-0-32b-gguf-on-rtx-4000-ada-laptop-12gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
