EXAONE 3.5 7.8B Instruct needs ~9.2 GB VRAM. Mac mini M2 24GB has 17.3 GB. With Q4_K_M quantization, expect ~14 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
13.7 tok/s
TTFT
14172 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 | 13.7 tok/s | 7730 ms | 158K |
| Coding | C | Runs well | 13.7 tok/s | 14172 ms | 158K |
| Agentic Coding | C | Runs well | 13.7 tok/s | 20613 ms | 158K |
| Reasoning | C | Runs well | 13.7 tok/s | 16748 ms | 158K |
| RAG | C | Runs well | 13.7 tok/s | 25766 ms | 158K |
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on Mac mini M2 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 3.5 7.8B Instruct on your machine.
Run
lms load hf-lmstudio-community--exaone-3-5-7-8b-instruct-gguf && lms server startUpgrade options
Raises estimated decode speed by about 256%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 115%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Raises estimated decode speed by about 612%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M2 24GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 13.7 tok/s.
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 3.5 7.8B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Mac mini M2 24GB, EXAONE 3.5 7.8B Instruct achieves approximately 13.7 tokens per second decode speed with a time-to-first-token of 14172ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct on Mac mini M2 24GB receives a C grade with 13.7 tok/s and 158K context.
On Mac mini M2 24GB, EXAONE 3.5 7.8B Instruct 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-lmstudio-community--exaone-3-5-7-8b-instruct-gguf-on-m2-24gb" 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. Mac mini M2 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.