EXAONE 3.5 7.8B Instruct needs ~13.5 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~17 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
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 | 16.7 tok/s | 6321 ms | 587K |
| Coding | C | Runs well | 16.7 tok/s | 11589 ms | 587K |
| Agentic Coding | C | Runs well | 16.7 tok/s | 16856 ms | 587K |
| Reasoning | C | Runs well | 16.7 tok/s | 13696 ms | 587K |
| RAG | C | Runs well | 16.7 tok/s | 21070 ms | 587K |
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on Mac mini M4 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 | C42 |
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 3.5 7.8B Instruct on your machine.
Run
lms load hf-lgai-exaone--exaone-3-5-7-8b-instruct-gguf && lms server startUpgrade options
Raises estimated decode speed by about 372%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 202%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Raises estimated decode speed by about 554%.
Adds memory headroom for longer context windows and future model growth.
~$3,999 MSRP
Yes, Mac mini M4 64GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 16.7 tok/s.
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 13.5 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 M4 64GB, EXAONE 3.5 7.8B Instruct achieves approximately 16.7 tokens per second decode speed with a time-to-first-token of 11589ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct on Mac mini M4 64GB receives a C grade with 16.7 tok/s and 587K context.
On Mac mini M4 64GB, EXAONE 3.5 7.8B Instruct 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. Mac mini M4 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-lgai-exaone--exaone-3-5-7-8b-instruct-gguf-on-m4-mini-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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