Would CUDA be a better path than Intel Arc A580 8GB 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.

Can Llama 3.2 11B Vision run on Intel Arc A580 8GB?

Q: What should I upgrade first if Llama 3.2 11B Vision feels slow on Intel Arc A580 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q3_K_S

C54Usable

Estimated from fit model

Llama 3.2 11B Vision needs ~9.3 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q3_K_S quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Llama 3.2 11B Vision at Q4_K_M needs 10.7 GB — too much for Intel Arc A580 8GB (8.0 GB). Runs at Q3_K_S (9.3 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.7 GB, exceeds 8.0 GB available

10.7 GB required8.0 GB available

134% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

16.5 tok/s

TTFT

11759 ms

Safe context

Memory

10.7 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3.2 11B Vision 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: 16.5 tok/s decode · 11.8s TTFT (warm) · 41 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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	F	Too heavy	20.2 tok/s	5240 ms	4K
Coding	F	Too heavy	16.5 tok/s	11759 ms	4K
Agentic Coding	F	Too heavy	11.6 tok/s	24371 ms	4K
Reasoning	F	Too heavy	16.5 tok/s	13897 ms	4K
RAG	F	Too heavy	11.6 tok/s	30464 ms	4K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	B68
Q3_K_SBest for your GPU	3	5.4 GB	Low	B67

Get started

Copy-paste commands to run Llama 3.2 11B Vision on your machine.

Run

ollama run llama3.2-vision:11b

Upgrade options

Hardware that runs Llama 3.2 11B Vision well

Intel Arc B570 10GBBest value

10 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.21.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 28%.

~$219 MSRP

Intel Arc B580 12GBBudget pick

12 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.35.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

Intel Arc A770 16GBIntel upgrade

16 GB VRAM (+8)560 GB/s (+48)

Makes the model fit on the accelerator instead of staying completely out of reach.40.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$349 MSRP

Frequently asked questions

Can Intel Arc A580 8GB run Llama 3.2 11B Vision?

Yes, Intel Arc A580 8GB can run Llama 3.2 11B Vision at Q3_K_S quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 10.7 GB which exceeds available memory, but at Q3_K_S it needs only 9.3 GB. Expected decode speed: 25.2 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 10.7 GB at Q4_K_M quantization. On Intel Arc A580 8GB, it fits at Q3_K_S using 9.3 GB.

What is the best quantization for Llama 3.2 11B Vision?

The recommended quantization is Q4_K_M, but on Intel Arc A580 8GB the best fitting quantization is Q3_K_S, which uses 9.3 GB.

What speed will Llama 3.2 11B Vision run at on Intel Arc A580 8GB?

On Intel Arc A580 8GB, Llama 3.2 11B Vision achieves approximately 25.2 tokens per second decode speed with a time-to-first-token of 7691ms using Q3_K_S quantization.

Can Intel Arc A580 8GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on Intel Arc A580 8GB receives a F grade with 16.5 tok/s and 4K context.

What context window can Llama 3.2 11B Vision use on Intel Arc A580 8GB?

On Intel Arc A580 8GB, Llama 3.2 11B Vision can safely use up to 5K tokens of context at Q3_K_S quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for Intel Arc A580 8GB See all hardware for Llama 3.2 11B Vision

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