K EXAONE 236B A23B needs ~185.9 GB but NVIDIA DGX Spark 128GB only has 108.8 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
77.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.0 tok/s
TTFT
96800 ms
Safe context
4K
Memory
185.9 GB / 108.8 GB
Offload
40%
Usable shared or unified memory is the main blocker for this model.
Not enough usable memory
The model needs 185.9 GB, but this setup only exposes 108.8 GB of usable shared or unified memory.
Move to a larger memory pool
A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 2.0 tok/s | 52800 ms | 4K |
| Coding | F | Too heavy | 2.0 tok/s | 96800 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.0 tok/s | 114400 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 DGX Spark 128GB (92.2 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 |
Upgrade options
NVIDIA B200 180GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 1710%.
~$30,000 MSRP
B100 192GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$35,000 MSRP
NVIDIA GB200 192GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$60,000 MSRP
No, K EXAONE 236B A23B requires more memory than NVIDIA DGX Spark 128GB provides.
K EXAONE 236B A23B (236B parameters) requires approximately 185.9 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 DGX Spark 128GB, K EXAONE 236B A23B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.
For coding workloads, K EXAONE 236B A23B on NVIDIA DGX Spark 128GB receives a F grade with 2.0 tok/s and 4K context.
On NVIDIA DGX Spark 128GB, 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.
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-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |
Move to a larger memory pool. A larger unified-memory SKU or a discrete high-bandwidth GPU is the cleanest way to make this model practical.
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
