K EXAONE 236B A23B needs ~200.2 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~3 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
15.9 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~11.4 GB host RAM)
Decode
3.4 tok/s
TTFT
57619 ms
Safe context
7K
Memory
200.2 GB / 184.3 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 11.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~1.6 GB host RAM) | 3.8 tok/s | 28047 ms | 7K |
| Coding | D | Very compromised (needs ~11.4 GB host RAM) | 3.4 tok/s | 57619 ms | 7K |
| Agentic Coding | F | Too heavy | 2.8 tok/s | 99158 ms | 7K |
| Reasoning | D | Very compromised (needs ~11.4 GB host RAM) | 3.4 tok/s | 68095 ms | 7K |
| RAG | F | Too heavy | 2.8 tok/s | 123947 ms | 7K |
How K EXAONE 236B A23B (236B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 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 | 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 |
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 startアップグレードオプション
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 1094%.
〜$8,000 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 744%.
〜$15,000 MSRP
Yes, Mac Studio M3 Ultra 256GB can run K EXAONE 236B A23B with a D grade (Very compromised (needs ~11.4 GB host RAM)). Expected decode speed: 3.4 tok/s.
K EXAONE 236B A23B (236B parameters) requires approximately 200.2 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 Mac Studio M3 Ultra 256GB, K EXAONE 236B A23B achieves approximately 3.4 tokens per second decode speed with a time-to-first-token of 57619ms using Q4_K_M quantization.
For coding workloads, K EXAONE 236B A23B on Mac Studio M3 Ultra 256GB receives a D grade with 3.4 tok/s and 7K context.
On Mac Studio M3 Ultra 256GB, K EXAONE 236B A23B can safely use up to 7K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Not always. Mac Studio M3 Ultra 256GB 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.
Paste this snippet into any page to show a live fit card.
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