K EXAONE 236B A23B needs ~133.4 GB VRAM. AMD Instinct MI250X 128GB has 128.0 GB. With Q2_K quantization, expect ~16 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
57.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.0 tok/s
TTFT
32443 ms
Safe context
4K
Memory
185.3 GB / 128.0 GB
Offload
30%
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 | F | Too heavy | 7.0 tok/s | 15031 ms | 4K |
| Coding | F | Too heavy | 6.0 tok/s | 32443 ms | 4K |
| Agentic Coding | F | Too heavy | 4.5 tok/s | 63243 ms | 4K |
| Reasoning | F | Too heavy | 6.0 tok/s | 38342 ms | 4K |
| RAG | F | Too heavy | 4.5 tok/s | 79054 ms | 4K |
How K EXAONE 236B A23B (236B params) fits at each quantization level on AMD Instinct MI250X 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 92.0 GB | Low | C48 |
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 |
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 startOpções de upgrade
Makes 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.
~$8,000 MSRP
Makes 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.
~$15,000 MSRP
Makes 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.
~$20,000 MSRP
Yes, AMD Instinct MI250X 128GB can run K EXAONE 236B A23B at Q2_K quantization (Runs with offload (needs ~3.7 GB host RAM)). The recommended Q4_K_M requires 185.3 GB which exceeds available memory, but at Q2_K it needs only 133.4 GB. Expected decode speed: 15.8 tok/s.
K EXAONE 236B A23B (236B parameters) requires approximately 185.3 GB at Q4_K_M quantization. On AMD Instinct MI250X 128GB, it fits at Q2_K using 133.4 GB.
The recommended quantization is Q4_K_M, but on AMD Instinct MI250X 128GB the best fitting quantization is Q2_K, which uses 133.4 GB.
On AMD Instinct MI250X 128GB, K EXAONE 236B A23B achieves approximately 15.8 tokens per second decode speed with a time-to-first-token of 12215ms using Q2_K quantization.
For coding workloads, K EXAONE 236B A23B on AMD Instinct MI250X 128GB receives a F grade with 6.0 tok/s and 4K context.
On AMD Instinct MI250X 128GB, K EXAONE 236B A23B can safely use up to 13K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-instinct-mi250x-128gb" 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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