Intel Arc B580 12GBBudget pick456 GB/s (+256)
C
Raises estimated decode speed by about 68%.46 tok/s decode
Raises estimated decode speed by about 68%.
~$249 MSRP
EXAONE 3.5 7.8B Instruct needs ~8.3 GB VRAM. MacBook Pro M1 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~27 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
27.3 tok/s
TTFT
7086 ms
Safe context
72K
Memory
8.3 GB / 11.5 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 | 27.3 tok/s | 3865 ms | 72K |
| Coding | C | Runs well | 27.3 tok/s | 7086 ms | 72K |
| Agentic Coding | C | Runs well | 27.3 tok/s | 10307 ms | 72K |
| Reasoning | C | Runs well | 27.3 tok/s | 8374 ms | 72K |
| RAG | C | Runs well | 27.3 tok/s | 12883 ms | 72K |
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on MacBook Pro M1 Pro 16GB (11.5 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C49 |
Q3_K_S | 3 | 3.8 GB | Low | C51 |
NVFP4 | 4 |
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
Intel Arc B580 12GBBudget pickRaises estimated decode speed by about 68%.
~$249 MSRP
RTX 3060 12GBBest valueRaises estimated decode speed by about 53%.
~$329 MSRP
Yes, MacBook Pro M1 Pro 16GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 27.3 tok/s.
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 8.3 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 MacBook Pro M1 Pro 16GB, EXAONE 3.5 7.8B Instruct achieves approximately 27.3 tokens per second decode speed with a time-to-first-token of 7086ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct on MacBook Pro M1 Pro 16GB receives a C grade with 27.3 tok/s and 72K context.
On MacBook Pro M1 Pro 16GB, EXAONE 3.5 7.8B Instruct can safely use up to 72K 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-lgai-exaone--exaone-3-5-7-8b-instruct-gguf-on-m1-pro-16gb" 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 |
| C51 |
Q4_K_M | 4 | 4.8 GB | Medium | C52 |
Q5_K_M | 5 | 5.6 GB | High | C52 |
Q6_K | 6 | 6.4 GB | High | C52 |
Q8_0Best for your GPU | 8 | 8.3 GB | Very High | C51 |
F16 | 16 | 16.0 GB | Maximum | F0 |
Not always. MacBook Pro M1 Pro 16GB 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.