exaone 3.0 7.8b it needs ~9.2 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~18 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
16.7 tok/s
TTFT
11589 ms
Safe context
158K
Memory
9.2 GB / 17.3 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 | 16.7 tok/s | 6321 ms | 158K |
| Coding | C | Runs well | 18.2 tok/s | 10661 ms | 158K |
| Agentic Coding | C | Runs well | 16.7 tok/s | 16856 ms | 158K |
| Reasoning | C | Runs well | 16.7 tok/s | 13696 ms | 158K |
| RAG | C | Runs well | 16.7 tok/s | 21070 ms | 158K |
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C46 |
Q3_K_S | 3 | 3.8 GB | Low | C47 |
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 192%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 254%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 177%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, MacBook Air M4 24GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 18.2 tok/s.
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 9.2 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 Air M4 24GB, exaone 3.0 7.8b it achieves approximately 18.2 tokens per second decode speed with a time-to-first-token of 10661ms using Q4_K_M quantization.
For coding workloads, exaone 3.0 7.8b it on MacBook Air M4 24GB receives a C grade with 18.2 tok/s and 158K context.
On MacBook Air M4 24GB, exaone 3.0 7.8b it can safely use up to 158K 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-m4-air-24gb" 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 |
| C47 |
Q4_K_M | 4 | 4.8 GB | Medium | C48 |
Q5_K_M | 5 | 5.6 GB | High | C48 |
Q6_K | 6 | 6.4 GB | High | C49 |
Q8_0Best for your GPU | 8 | 8.3 GB | Very High | C51 |
F16 | 16 | 16.0 GB | Maximum | F0 |
Not always. MacBook Air M4 24GB 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.