MacBook Pro M3 Pro 18GBBudget pick
18 GB Unified (+6)
C
This setup is broadly balanced for this model.33.6 tok/s decode
~$1,999 MSRP
EXAONE 3.5 2.4B Instruct needs ~3.8 GB VRAM. Intel Arc A730M 12GB has 12.0 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
480K
Memory
3.8 GB / 12.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 33.6 tok/s | 3143 ms | 480K |
| Coding | C | Runs well | 33.6 tok/s | 5762 ms | 480K |
| Agentic Coding | C | Runs well | 33.6 tok/s | 8381 ms | 480K |
| Reasoning | C | Runs well | 33.6 tok/s | 6810 ms | 480K |
| RAG | C | Runs well | 33.6 tok/s | 10476 ms | 480K |
How EXAONE 3.5 2.4B Instruct (2.4000000953674316B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.9 GB | Low | C47 |
Q3_K_S | 3 | 1.2 GB | Low | C47 |
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 startUpgrade options
Yes, Intel Arc A730M 12GB 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 3.8 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 Intel Arc A730M 12GB, 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 Intel Arc A730M 12GB receives a C grade with 33.6 tok/s and 480K context.
On Intel Arc A730M 12GB, EXAONE 3.5 2.4B Instruct can safely use up to 480K 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-arc-a730m-12gb" 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 |
| C47 |
Q4_K_M | 4 | 1.5 GB | Medium | C47 |
Q5_K_M | 5 | 1.7 GB | High | C48 |
Q6_K | 6 | 2.0 GB | High | C48 |
Q8_0 | 8 | 2.6 GB | Very High | C49 |
F16Best for your GPU | 16 | 4.9 GB | Maximum | C52 |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.