exaone 3.0 7.8b it needs ~7.8 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~40 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
39.5 tok/s
TTFT
4896 ms
Safe context
90K
Memory
7.8 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 | 39.5 tok/s | 2670 ms | 90K |
| Coding | C | Runs well | 39.5 tok/s | 4896 ms | 90K |
| Agentic Coding | C | Runs well | 39.5 tok/s | 7121 ms | 90K |
| Reasoning | C | Runs well | 39.5 tok/s | 5786 ms | 90K |
| RAG | C | Runs well | 39.5 tok/s | 8901 ms | 90K |
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C49 |
Q3_K_S | 3 | 3.8 GB | Low | C50 |
NVFP4 | 4 |
Copy-paste commands to run exaone 3.0 7.8b it on your machine.
Run
lms load hf-bingsu--exaone-3-0-7-8b-it && lms server startYes, Intel Arc Pro A60 12GB can run exaone 3.0 7.8b it with a C grade (Runs well). Expected decode speed: 39.5 tok/s.
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.
The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro A60 12GB, exaone 3.0 7.8b it achieves approximately 39.5 tokens per second decode speed with a time-to-first-token of 4896ms using Q4_K_M quantization.
For coding workloads, exaone 3.0 7.8b it on Intel Arc Pro A60 12GB receives a C grade with 39.5 tok/s and 90K context.
On Intel Arc Pro A60 12GB, exaone 3.0 7.8b it can safely use up to 90K 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-bingsu--exaone-3-0-7-8b-it-on-arc-pro-a60-12gb" 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 | C51 |
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 |
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.