EXAONE 4.0 32B needs ~27.6 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~6 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
4.6 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~3.2 GB host RAM)
Decode
5.5 tok/s
TTFT
35412 ms
Safe context
4K
Memory
27.6 GB / 23.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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 3.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~2.1 GB host RAM) | 6.0 tok/s | 17636 ms | 4K |
| Coding | D | Very compromised (needs ~3.2 GB host RAM) | 5.5 tok/s | 35412 ms | 4K |
| Agentic Coding | F | Too heavy | 4.7 tok/s | 60065 ms | 4K |
| Reasoning | D | Very compromised (needs ~3.2 GB host RAM) | 5.5 tok/s | 41851 ms | 4K |
| RAG | F | Too heavy | 4.7 tok/s | 75082 ms | 4K |
How EXAONE 4.0 32B (32B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C50 |
Q3_K_SBest for your GPU | 3 | 15.7 GB | Low | C49 |
NVFP4 | 4 | 17.9 GB | Medium | F0 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Copy-paste commands to run EXAONE 4.0 32B on your machine.
Run
lms load hf-lgai-exaone--exaone-4-0-32b-gguf && lms server start升级选项
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 45%.
~$1,099 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 253%.
~$1,599 MSRP
~$1,999 MSRP
Yes, MacBook Pro M2 Pro 32GB can run EXAONE 4.0 32B with a D grade (Very compromised (needs ~3.2 GB host RAM)). Expected decode speed: 5.5 tok/s.
EXAONE 4.0 32B (32B parameters) requires approximately 27.6 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 4.0 32B is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, EXAONE 4.0 32B achieves approximately 5.5 tokens per second decode speed with a time-to-first-token of 35412ms using Q4_K_M quantization.
For coding workloads, EXAONE 4.0 32B on MacBook Pro M2 Pro 32GB receives a D grade with 5.5 tok/s and 4K context.
On MacBook Pro M2 Pro 32GB, EXAONE 4.0 32B can safely use up to 4K 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. MacBook Pro M2 Pro 32GB 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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