EXAONE 3.5 7.8B Instruct needs ~7.8 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~46 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
46.0 tok/s
TTFT
4208 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 | 46.0 tok/s | 2296 ms | 90K |
| Coding | C | Runs well | 46.0 tok/s | 4208 ms | 90K |
| Agentic Coding | C | Runs well | 46.0 tok/s | 6121 ms | 90K |
| Reasoning | C | Runs well | 46.0 tok/s | 4974 ms | 90K |
| RAG | C | Runs well | 46.0 tok/s | 7652 ms | 90K |
How EXAONE 3.5 7.8B Instruct (7.800000190734863B params) fits at each quantization level on Intel Arc B580 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 3.5 7.8B Instruct on your machine.
Run
lms load hf-lgai-exaone--exaone-3-5-7-8b-instruct-gguf && lms server startYes, Intel Arc B580 12GB can run EXAONE 3.5 7.8B Instruct with a C grade (Runs well). Expected decode speed: 46.0 tok/s.
EXAONE 3.5 7.8B Instruct (7.800000190734863B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.
The recommended quantization for EXAONE 3.5 7.8B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc B580 12GB, EXAONE 3.5 7.8B Instruct achieves approximately 46.0 tokens per second decode speed with a time-to-first-token of 4208ms using Q4_K_M quantization.
For coding workloads, EXAONE 3.5 7.8B Instruct on Intel Arc B580 12GB receives a C grade with 46.0 tok/s and 90K context.
On Intel Arc B580 12GB, EXAONE 3.5 7.8B Instruct 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-lgai-exaone--exaone-3-5-7-8b-instruct-gguf-on-arc-b580-12gb" 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.