EXAONE 4.0 32B needs ~28.1 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 GB. With Q4_K_M quantization, expect ~5 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
2.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.5 GB host RAM)
Decode
4.9 tok/s
TTFT
39556 ms
Safe context
7K
Memory
28.1 GB / 25.9 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 {ram} 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 ~0.2 GB host RAM) | 5.5 tok/s | 19312 ms | 7K |
| Coding | D | Very compromised | 4.9 tok/s | 39556 ms | 7K |
| Agentic Coding | F | Too heavy | 4.2 tok/s | 67781 ms | 7K |
| Reasoning | D | Very compromised (needs ~1.5 GB host RAM) | 4.9 tok/s | 46748 ms | 7K |
| RAG | F | Too heavy | 4.2 tok/s | 84726 ms | 7K |
How EXAONE 4.0 32B (32B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | C50 |
Q3_K_S | 3 | 15.7 GB | Low | C49 |
NVFP4 | 4 | 17.9 GB | Medium | C49 |
Q4_K_MBest for your GPU | 4 | 19.5 GB | Medium | C49 |
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 startOpciones de mejora
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 63%.
~$1,099 MSRP
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 296%.
~$1,599 MSRP
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
Sube la velocidad estimada de decodificación alrededor de un 529%.
~$2,499 MSRP
Yes, MacBook Pro M3 Pro 36GB can run EXAONE 4.0 32B with a D grade (Very compromised). Expected decode speed: 4.9 tok/s.
EXAONE 4.0 32B (32B parameters) requires approximately 28.1 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 M3 Pro 36GB, EXAONE 4.0 32B achieves approximately 4.9 tokens per second decode speed with a time-to-first-token of 39556ms using Q4_K_M quantization.
For coding workloads, EXAONE 4.0 32B on MacBook Pro M3 Pro 36GB receives a D grade with 4.9 tok/s and 7K context.
On MacBook Pro M3 Pro 36GB, EXAONE 4.0 32B 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. MacBook Pro M3 Pro 36GB 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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