EXAONE 4.0 1.2B needs ~2.2 GB VRAM. Intel Arc A370M 4GB has 4.0 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
16.8 tok/s
TTFT
11524 ms
Safe context
224K
Memory
2.2 GB / 4.0 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 | 16.8 tok/s | 6286 ms | 157K |
| Coding | C | Runs well | 16.8 tok/s | 11524 ms | 224K |
| Agentic Coding | C | Runs well | 16.8 tok/s | 16762 ms | 224K |
| Reasoning | C | Runs well | 16.8 tok/s | 13619 ms | 224K |
| RAG | C | Runs well | 16.8 tok/s | 20952 ms | 224K |
How EXAONE 4.0 1.2B (1.2000000476837158B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.5 GB | Low | B55 |
Q3_K_S | 3 | 0.6 GB | Low | B56 |
NVFP4 | 4 |
Copy-paste commands to run EXAONE 4.0 1.2B on your machine.
Run
lms load hf-lgai-exaone--exaone-4-0-1-2b-gguf && lms server startYes, Intel Arc A370M 4GB can run EXAONE 4.0 1.2B with a C grade (Runs well). Expected decode speed: 16.8 tok/s.
EXAONE 4.0 1.2B (1.2000000476837158B parameters) requires approximately 2.2 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 4.0 1.2B is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A370M 4GB, EXAONE 4.0 1.2B achieves approximately 16.8 tokens per second decode speed with a time-to-first-token of 11524ms using Q4_K_M quantization.
For coding workloads, EXAONE 4.0 1.2B on Intel Arc A370M 4GB receives a C grade with 16.8 tok/s and 224K context.
On Intel Arc A370M 4GB, EXAONE 4.0 1.2B can safely use up to 224K 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-4-0-1-2b-gguf-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| B56 |
Q4_K_M | 4 | 0.7 GB | Medium | B56 |
Q5_K_M | 5 | 0.9 GB | High | B56 |
Q6_K | 6 | 1.0 GB | High | B55 |
Q8_0Best for your GPU | 8 | 1.3 GB | Very High | B55 |
F16 | 16 | 2.5 GB | Maximum | F0 |
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.