Intel Arc A770 16GBGünstige Wahl16 GB VRAM (+4)560 GB/s (+104)
B
Adds memory headroom for longer context windows and future model growth.40.4 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $349 MSRP
Can Llama 3.2 11B Vision run on Intel Arc B580 12GB?
YES — Tight Fit
B65Good
Estimated from fit model
Llama 3.2 11B Vision needs ~11.1 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~35 tok/s.
Runtime: OllamaCapacity: TightBandwidth: MediumStack: BasicBottleneck: 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) — 11.1 GB, 35.1 tok/s, Tight fit
11.1 GB required12.0 GB available
93% VRAM used
Fit status
Tight fit
Decode
35.1 tok/s
TTFT
5521 ms
Safe context
16K
Memory
11.1 GB / 12.0 GB
Memory breakdown
Weights6.7 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsLlama 3.2 11B Vision on Intel Arc B580 12GB
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: 35.1 tok/s decode · 5.5s TTFT (warm) · 88 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 | B | Tight fit | 35.1 tok/s | 3011 ms | 16K |
| Coding | B | Tight fit | 35.1 tok/s | 5521 ms | 16K |
| Agentic Coding | C | Very compromised (needs ~0.5 GB host RAM) | 22.7 tok/s | 12396 ms | 16K |
| Reasoning | B | Tight fit | 35.1 tok/s | 6525 ms | 16K |
| RAG | C | Very compromised (needs ~0.5 GB host RAM) | 22.7 tok/s | 15495 ms | 16K |
Quantization options
How Llama 3.2 11B Vision (11B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.3 GB | Low | B65 |
Q3_K_S | 3 | 5.4 GB | Low | B67 |
NVFP4 | 4 | 6.2 GB | Medium | B67 |
Q4_K_M | 4 | 6.7 GB | Medium | B66 |
Q5_K_M | 5 | 7.9 GB | High | B66 |
Q6_KBest for your GPU | 6 | 9.0 GB | High | B66 |
Q8_0 | 8 | 11.8 GB | Very High | F0 |
F16 | 16 | 22.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Llama 3.2 11B Vision on your machine.
Run
ollama run llama3.2-vision:11bUpgrade-Optionen
Hardware, die Llama 3.2 11B Vision gut ausführt
Frequently asked questions
Can Intel Arc B580 12GB run Llama 3.2 11B Vision?
Yes, Intel Arc B580 12GB can run Llama 3.2 11B Vision with a B grade (Tight fit). Expected decode speed: 35.1 tok/s.
How much VRAM does Llama 3.2 11B Vision need?
Llama 3.2 11B Vision (11B parameters) requires approximately 11.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Llama 3.2 11B Vision?
The recommended quantization for Llama 3.2 11B Vision is Q4_K_M, which balances quality and memory efficiency.
What speed will Llama 3.2 11B Vision run at on Intel Arc B580 12GB?
On Intel Arc B580 12GB, Llama 3.2 11B Vision achieves approximately 35.1 tokens per second decode speed with a time-to-first-token of 5521ms using Q4_K_M quantization.
Can Intel Arc B580 12GB run Llama 3.2 11B Vision for coding?
For coding workloads, Llama 3.2 11B Vision on Intel Arc B580 12GB receives a B grade with 35.1 tok/s and 16K context.
What context window can Llama 3.2 11B Vision use on Intel Arc B580 12GB?
On Intel Arc B580 12GB, Llama 3.2 11B Vision can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
What should I upgrade first if Llama 3.2 11B Vision feels slow on Intel Arc B580 12GB?
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 B580 12GB for Llama 3.2 11B Vision?
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.
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