Intel Arc B570 10GBコスパ重視10 GB VRAM (+2)
C
Adds memory headroom for longer context windows and future model growth.43.1 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$219 MSRP
Can exaone 3.0 7.8b it run on Intel Arc A580 8GB?
YES — Tight Fit
C52Usable
Estimated from fit model
exaone 3.0 7.8b it needs ~7.4 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~53 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 7.4 GB, 52.7 tok/s, Tight fit
7.4 GB required8.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
52.7 tok/s
TTFT
3672 ms
Safe context
27K
Memory
7.4 GB / 8.0 GB
Memory breakdown
Weights4.8 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsexaone 3.0 7.8b it on Intel Arc A580 8GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 52.7 tok/s decode · 3.7s TTFT (warm) · 132 tok/s prefill
What limits this setup
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
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.
Best improvement path
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 52.7 tok/s | 2003 ms | 27K |
| Coding | C | Tight fit | 52.7 tok/s | 3672 ms | 27K |
| Agentic Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 36.7 tok/s | 7668 ms | 27K |
| Reasoning | C | Tight fit | 52.7 tok/s | 4339 ms | 27K |
| RAG | C | Runs with offload (needs ~0.2 GB host RAM) | 36.7 tok/s | 9584 ms | 27K |
Quantization options
How exaone 3.0 7.8b it (7.800000190734863B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.0 GB | Low | C54 |
Q3_K_S | 3 | 3.8 GB | Low | C53 |
NVFP4 | 4 | 4.4 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.8 GB | Medium | C53 |
Q5_K_M | 5 | 5.6 GB | High | F0 |
Q6_K | 6 | 6.4 GB | High | F0 |
Q8_0 | 8 | 8.3 GB | Very High | F0 |
F16 | 16 | 16.0 GB | Maximum | F0 |
Get started
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 startアップグレードオプション
exaone 3.0 7.8b itを快適に動かすハードウェア
Intel Arc B570 10GBコスパ重視Intel Arc B580 12GBコスパ最良12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.46 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$249 MSRP
Intel Arc Pro A60 12GBIntelアップグレード12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.39.5 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$499 MSRP
Frequently asked questions
Can Intel Arc A580 8GB run exaone 3.0 7.8b it?
Yes, Intel Arc A580 8GB can run exaone 3.0 7.8b it with a C grade (Tight fit). Expected decode speed: 52.7 tok/s.
How much VRAM does exaone 3.0 7.8b it need?
exaone 3.0 7.8b it (7.800000190734863B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.
What is the best quantization for exaone 3.0 7.8b it?
The recommended quantization for exaone 3.0 7.8b it is Q4_K_M, which balances quality and memory efficiency.
What speed will exaone 3.0 7.8b it run at on Intel Arc A580 8GB?
On Intel Arc A580 8GB, exaone 3.0 7.8b it achieves approximately 52.7 tokens per second decode speed with a time-to-first-token of 3672ms using Q4_K_M quantization.
Can Intel Arc A580 8GB run exaone 3.0 7.8b it for coding?
For coding workloads, exaone 3.0 7.8b it on Intel Arc A580 8GB receives a C grade with 52.7 tok/s and 27K context.
What context window can exaone 3.0 7.8b it use on Intel Arc A580 8GB?
On Intel Arc A580 8GB, exaone 3.0 7.8b it can safely use up to 27K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if exaone 3.0 7.8b it feels slow on Intel Arc A580 8GB?
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.
Would CUDA be a better path than Intel Arc A580 8GB for exaone 3.0 7.8b it?
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.
Embed this result▼
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-a580-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: