exaone 3.0 7.8b it needs ~13.5 GB VRAM. MacBook Pro M4 Pro 64GB has 46.1 GB. With Q4_K_M quantization, expect ~41 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
40.6 tok/s
TTFT
4763 ms
Safe context
587K
Memory
13.5 GB / 46.1 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 | 40.6 tok/s | 2598 ms | 587K |
| Coding | C | Runs well | 40.6 tok/s | 4763 ms | 587K |
| Agentic Coding | C | Runs well | 40.6 tok/s | 6928 ms | 587K |
| Reasoning | C | Runs well | 40.6 tok/s | 5629 ms | 587K |
| RAG | C | Runs well | 40.6 tok/s | 8660 ms | 587K |
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on MacBook Pro M4 Pro 64GB (46.1 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C41 |
Q3_K_S | 3 | 3.8 GB | Low | C41 |
NVFP4 | 4 | 4.4 GB | Medium | C41 |
Q4_K_M | 4 | 4.8 GB | Medium | C42 |
Q5_K_M | 5 | 5.6 GB | High | C42 |
Q6_K | 6 | 6.4 GB | High | C42 |
Q8_0 | 8 | 8.3 GB | Very High | C42 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | C45 |
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-Optionen
Raises estimated decode speed by about 94%.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
Raises estimated decode speed by about 169%.
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Raises estimated decode speed by about 140%.
Adds memory headroom for longer context windows and future model growth.
ca. $3,999 MSRP
Yes, MacBook Pro M4 Pro 64GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 40.6 tok/s.
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 13.5 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 M4 Pro 64GB, exaone 3.0 7.8b it achieves approximately 40.6 tokens per second decode speed with a time-to-first-token of 4763ms using Q4_K_M quantization.
For coding workloads, exaone 3.0 7.8b it on MacBook Pro M4 Pro 64GB receives a C grade with 40.6 tok/s and 587K context.
On MacBook Pro M4 Pro 64GB, exaone 3.0 7.8b it can safely use up to 587K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Not always. MacBook Pro M4 Pro 64GB 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.
<iframe src="https://willitrunai.com/embed/hf-bingsu--exaone-3-0-7-8b-it-on-m4-pro-64gb" 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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