EXAONE 3.5 7.8B Instruct i1 needs ~20.4 GB VRAM. MacBook Pro M3 Max 128GB has 92.2 GB. With Q4_K_M quantization, expect ~50 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
50.4 tok/s
TTFT
3838 ms
Safe context
1.3M
Memory
20.4 GB / 92.2 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 | 50.4 tok/s | 2094 ms | 1.3M |
| Coding | C | Runs well | 50.4 tok/s | 3838 ms | 1.3M |
| Agentic Coding | C | Runs well | 50.4 tok/s | 5583 ms | 1.3M |
| Reasoning | C | Runs well | 50.4 tok/s | 4536 ms | 1.3M |
| RAG | C | Runs well | 50.4 tok/s | 6978 ms | 1.3M |
How EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B params) fits at each quantization level on MacBook Pro M3 Max 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | D39 |
Q3_K_S | 3 | 3.8 GB | Low | D39 |
NVFP4 | 4 |
Copy-paste commands to run EXAONE 3.5 7.8B Instruct i1 on your machine.
Run
lms load hf-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf && lms server startUpgrade options
Raises estimated decode speed by about 117%.
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBBest valueRaises estimated decode speed by about 117%.
~$9,999 MSRP
Yes, MacBook Pro M3 Max 128GB can run EXAONE 3.5 7.8B Instruct i1 with a C grade (Runs well). Expected decode speed: 50.4 tok/s.
EXAONE 3.5 7.8B Instruct i1 (7.800000190734863B parameters) requires approximately 20.4 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 3.5 7.8B Instruct i1 is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Max 128GB, EXAONE 3.5 7.8B Instruct i1 achieves approximately 50.4 tokens per second decode speed with a time-to-first-token of 3838ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct i1 on MacBook Pro M3 Max 128GB receives a C grade with 50.4 tok/s and 1.3M context.
On MacBook Pro M3 Max 128GB, EXAONE 3.5 7.8B Instruct i1 can safely use up to 1.3M 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-mradermacher--exaone-3-5-7-8b-instruct-i1-gguf-on-m3-max-128gb" 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 |
| D39 |
Q4_K_M | 4 | 4.8 GB | Medium | D39 |
Q5_K_M | 5 | 5.6 GB | High | D39 |
Q6_K | 6 | 6.4 GB | High | D39 |
Q8_0 | 8 | 8.3 GB | Very High | D39 |
F16Best for your GPU | 16 | 16.0 GB | Maximum | D40 |
Not always. MacBook Pro M3 Max 128GB 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.