Raises estimated decode speed by about 28%.
~$35,000 MSRP
K EXAONE 236B A23B needs ~191.3 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~36 tok/s.
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
3.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~2.5 GB host RAM)
Decode
36.4 tok/s
TTFT
5323 ms
Safe context
14K
Memory
191.3 GB / 188.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 43.9 tok/s | 2406 ms | 14K |
| Coding | C | Runs with offload (needs ~2.5 GB host RAM) | 36.4 tok/s | 5323 ms | 14K |
| Agentic Coding | D | Very compromised (needs ~20.4 GB host RAM) | 29.1 tok/s | 9675 ms | 14K |
| Reasoning | C | Runs with offload (needs ~2.5 GB host RAM) | 36.4 tok/s | 6291 ms | 14K |
| RAG | D | Very compromised (needs ~20.4 GB host RAM) | 29.1 tok/s |
How K EXAONE 236B A23B (236B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 92.0 GB | Low | C47 |
Q3_K_S | 3 | 115.6 GB | Low | C48 |
NVFP4 | 4 |
Copy-paste commands to run K EXAONE 236B A23B on your machine.
Run
lms load hf-lgai-exaone--k-exaone-236b-a23b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 28%.
~$35,000 MSRP
Raises estimated decode speed by about 28%.
~$60,000 MSRP
Yes, H100 NVL 188GB can run K EXAONE 236B A23B with a C grade (Runs with offload (needs ~2.5 GB host RAM)). Expected decode speed: 36.4 tok/s.
K EXAONE 236B A23B (236B parameters) requires approximately 191.3 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 H100 NVL 188GB, K EXAONE 236B A23B achieves approximately 36.4 tokens per second decode speed with a time-to-first-token of 5323ms using Q4_K_M quantization.
For coding workloads, K EXAONE 236B A23B on H100 NVL 188GB receives a C grade with 36.4 tok/s and 14K context.
On H100 NVL 188GB, K EXAONE 236B A23B can safely use up to 14K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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-h100-nvl-188gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 12094 ms |
| 14K |
132.2 GB |
| Medium |
| C48 |
Q4_K_MBest for your GPU | 4 | 144.0 GB | Medium | C48 |
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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.