MacBook Pro M4 Max 36GBBudget pick
36 GB Unified (+4)410 GB/s (+210)
C
Raises estimated decode speed by about 101%.59.2 tok/s decode
Raises estimated decode speed by about 101%.
~$2,499 MSRP
exaone 3.0 7.8b it needs ~10.0 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~29 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
Fit status
Runs well
Decode
29.4 tok/s
TTFT
6580 ms
Safe context
244K
Memory
10.0 GB / 23.0 GB
This setup is broadly balanced for this model.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 29.4 tok/s | 3589 ms | 244K |
| Coding | C | Runs well | 29.4 tok/s | 6580 ms | 244K |
| Agentic Coding | C | Runs well | 29.4 tok/s | 9570 ms | 244K |
| Reasoning | C | Runs well | 29.4 tok/s | 7776 ms | 244K |
| RAG | C | Runs well | 29.4 tok/s | 11963 ms | 244K |
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C44 |
Q3_K_S | 3 | 3.8 GB | Low | C45 |
NVFP4 | 4 |
Copy-paste commands to run exaone 3.0 7.8b it on your machine.
Run
lms load hf-bingsu--exaone-3-0-7-8b-it && lms server startUpgrade options
Raises estimated decode speed by about 101%.
~$2,499 MSRP
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 232%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, MacBook Pro M2 Pro 32GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 29.4 tok/s.
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.
The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M2 Pro 32GB, exaone 3.0 7.8b it achieves approximately 29.4 tokens per second decode speed with a time-to-first-token of 6580ms using Q4_K_M quantization.
For coding workloads, exaone 3.0 7.8b it on MacBook Pro M2 Pro 32GB receives a C grade with 29.4 tok/s and 244K context.
On MacBook Pro M2 Pro 32GB, exaone 3.0 7.8b it can safely use up to 244K 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-bingsu--exaone-3-0-7-8b-it-on-m2-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.4 GB |
| Medium |
| C45 |
Q4_K_M | 4 | 4.8 GB | Medium | C45 |
Q5_K_M | 5 | 5.6 GB | High | C46 |
Q6_K | 6 | 6.4 GB | High | C46 |
Q8_0 | 8 | 8.3 GB | Very High | C48 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | C50 |
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.