K EXAONE 236B A23B needs ~180.8 GB but NVIDIA A800 80GB only has 80.0 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
100.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.3 tok/s
TTFT
83137 ms
Safe context
4K
Memory
180.8 GB / 80.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 180.8 GB, but this setup only exposes 80.0 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 | 2.7 tok/s | 39769 ms | 4K |
| Coding | F | Too heavy | 2.3 tok/s | 83137 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.3 tok/s | 98253 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How K EXAONE 236B A23B (236B params) fits at each quantization level on NVIDIA A800 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 92.0 GB | Low | F0 |
Q3_K_S | 3 | 115.6 GB | Low | F0 |
NVFP4 | 4 | 132.2 GB | Medium | F0 |
Q4_K_M | 4 | 144.0 GB | Medium | F0 |
Q5_K_M | 5 | 169.9 GB | High | F0 |
Q6_K | 6 | 193.5 GB | High | F0 |
Q8_0 | 8 | 252.5 GB | Very High | F0 |
F16 | 16 | 483.8 GB | Maximum | F0 |
Opciones de mejora
NVIDIA B200 180GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 1474%.
~$30,000 MSRP
B100 192GBMejor relación calidad-precioHace 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.
~$35,000 MSRP
NVIDIA GB200 192GBMejora NVIDIAHace 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.
~$60,000 MSRP
No, K EXAONE 236B A23B requires more memory than NVIDIA A800 80GB provides.
K EXAONE 236B A23B (236B parameters) requires approximately 180.8 GB of memory with Q4_K_M quantization.
The recommended quantization for K EXAONE 236B A23B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A800 80GB, K EXAONE 236B A23B achieves approximately 2.3 tokens per second decode speed with a time-to-first-token of 83137ms using Q4_K_M quantization.
For coding workloads, K EXAONE 236B A23B on NVIDIA A800 80GB receives a F grade with 2.3 tok/s and 4K context.
On NVIDIA A800 80GB, K EXAONE 236B A23B 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--k-exaone-236b-a23b-gguf-on-a800-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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