EXAONE 3.5 2.4B Instruct needs ~6.5 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~34 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
33.6 tok/s
TTFT
5762 ms
Safe context
1.1M
Memory
6.5 GB / 25.9 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 | 33.6 tok/s | 3143 ms | 1.1M |
| Coding | C | Runs well | 33.6 tok/s | 5762 ms | 1.1M |
| Agentic Coding | C | Runs well | 33.6 tok/s | 8381 ms | 1.1M |
| Reasoning | C | Runs well | 33.6 tok/s | 6810 ms | 1.1M |
| RAG | C | Runs well | 33.6 tok/s | 10476 ms | 1.1M |
How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.9 GB | Low | C43 |
Q3_K_S | 3 | 1.2 GB | Low | C43 |
NVFP4 | 4 |
Copy-paste commands to run EXAONE 3.5 2.4B Instruct on your machine.
Run
lms load hf-lmstudio-community--exaone-3-5-2-4b-instruct-gguf && lms server startYes, MacBook Pro M4 Max 36GB can run EXAONE 3.5 2.4B Instruct with a C grade (Runs well). Expected decode speed: 33.6 tok/s.
EXAONE 3.5 2.4B Instruct (2.4000000953674316B parameters) requires approximately 6.5 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 3.5 2.4B Instruct is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M4 Max 36GB, EXAONE 3.5 2.4B Instruct achieves approximately 33.6 tokens per second decode speed with a time-to-first-token of 5762ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 2.4B Instruct on MacBook Pro M4 Max 36GB receives a C grade with 33.6 tok/s and 1.1M context.
On MacBook Pro M4 Max 36GB, EXAONE 3.5 2.4B Instruct can safely use up to 1.1M 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-3-5-2-4b-instruct-gguf-on-m4-max-36gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
1.3 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 1.5 GB | Medium | C43 |
Q5_K_M | 5 | 1.7 GB | High | C43 |
Q6_K | 6 | 2.0 GB | High | C43 |
Q8_0 | 8 | 2.6 GB | Very High | C44 |
F16Best for your GPU | 16 | 4.9 GB | Maximum | C45 |
Not always. MacBook Pro M4 Max 36GB 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.