Would CUDA be a better path than Intel Arc Pro B50 16GB 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 Pro B50 16GB?

Q: What should I upgrade first if Llama 3.2 11B Vision feels slow on Intel Arc Pro B50 16GB?

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.

YES — Runs Great

B66Good

Estimated from fit model

Llama 3.2 11B Vision needs ~11.5 GB VRAM. Intel Arc Pro B50 16GB has 16.0 GB. With Q4_K_M quantization, expect ~18 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth

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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.5 GB, 19.4 tok/s, Runs well

11.5 GB required16.0 GB available

72% VRAM used

Fit status

Runs well

Decode

19.4 tok/s

TTFT

9990 ms

Safe context

16K

Memory

11.5 GB / 16.0 GB

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsLlama 3.2 11B Vision on Intel Arc Pro B50 16GB
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: 19.4 tok/s decode · 10.0s TTFT (warm) · 48 tok/s prefill

What limits this setup

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.

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	19.4 tok/s	5449 ms	16K
Coding	B	Runs well	18.0 tok/s	10740 ms	16K
Agentic Coding	B	Tight fit	19.4 tok/s	14531 ms	16K
Reasoning	B	Runs well	19.4 tok/s	11807 ms	16K
RAG	B	Tight fit	19.4 tok/s	18164 ms	16K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on Intel Arc Pro B50 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	B62
Q3_K_S	3	5.4 GB	Low	B64
NVFP4	4

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

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+576)

Raises estimated decode speed by about 296%.76.9 tok/s decode

Raises estimated decode speed by about 296%.

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+736)

Raises estimated decode speed by about 471%.110.7 tok/s decode

Raises estimated decode speed by about 471%.

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

Frequently asked questions

Can Intel Arc Pro B50 16GB run Llama 3.2 11B Vision?

Yes, Intel Arc Pro B50 16GB can run Llama 3.2 11B Vision with a B grade (Runs well). Expected decode speed: 18.0 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 11.5 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 Pro B50 16GB?

On Intel Arc Pro B50 16GB, Llama 3.2 11B Vision achieves approximately 18.0 tokens per second decode speed with a time-to-first-token of 10740ms using Q4_K_M quantization.

Can Intel Arc Pro B50 16GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on Intel Arc Pro B50 16GB receives a B grade with 18.0 tok/s and 16K context.

What context window can Llama 3.2 11B Vision use on Intel Arc Pro B50 16GB?

On Intel Arc Pro B50 16GB, 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.

See all results for Intel Arc Pro B50 16GB See all hardware for Llama 3.2 11B Vision

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