Would CUDA be a better path than Intel Arc B580 12GB for Pixtral 12B?

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 Pixtral 12B run on Intel Arc B580 12GB?

Q: What should I upgrade first if Pixtral 12B 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.

YES — With Offload

A74Great

Estimated from fit model

Pixtral 12B needs ~11.9 GB VRAM. Intel Arc B580 12GB has 12.0 GB. With Q4_K_M quantization, expect ~32 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.9 GB, 32.1 tok/s, Runs with offload

11.9 GB required12.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

32.1 tok/s

TTFT

6023 ms

Safe context

17K

Memory

11.9 GB / 12.0 GB

Memory breakdown

Weights7.3 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsPixtral 12B 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: 32.1 tok/s decode · 6.0s TTFT (warm) · 80 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	A	Tight fit	32.1 tok/s	3285 ms	17K
Coding	A	Runs with offload	32.1 tok/s	6023 ms	17K
Agentic Coding	B	Very compromised (needs ~1.2 GB host RAM)	17.2 tok/s	16389 ms	17K
Reasoning	A	Runs with offload	32.1 tok/s	7118 ms	17K
RAG	B	Very compromised (needs ~1.2 GB host RAM)	17.2 tok/s	20487 ms

Quantization options

How Pixtral 12B (12B params) fits at each quantization level on Intel Arc B580 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	A75
Q3_K_S	3	5.9 GB	Low	A76
NVFP4	4

Get started

Copy-paste commands to run Pixtral 12B on your machine.

Run

ollama run pixtral

Your hardware

More models your Intel Arc B580 12GB can run

Model	Params	Grade	Decode	Capabilities
Qwen 3 14B	14B	A	17.8 tok/s
Phi-4-reasoning-plus 14B	14.7B	A	14.4 tok/s

Frequently asked questions

Can Intel Arc B580 12GB run Pixtral 12B?

Yes, Intel Arc B580 12GB can run Pixtral 12B with a A grade (Runs with offload). Expected decode speed: 32.1 tok/s.

How much VRAM does Pixtral 12B need?

Pixtral 12B (12B parameters) requires approximately 11.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Pixtral 12B?

The recommended quantization for Pixtral 12B is Q4_K_M, which balances quality and memory efficiency.

What speed will Pixtral 12B run at on Intel Arc B580 12GB?

On Intel Arc B580 12GB, Pixtral 12B achieves approximately 32.1 tokens per second decode speed with a time-to-first-token of 6023ms using Q4_K_M quantization.

Can Intel Arc B580 12GB run Pixtral 12B for coding?

For coding workloads, Pixtral 12B on Intel Arc B580 12GB receives a A grade with 32.1 tok/s and 17K context.

What context window can Pixtral 12B use on Intel Arc B580 12GB?

On Intel Arc B580 12GB, Pixtral 12B can safely use up to 17K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Pixtral 12B feels slow on Intel Arc B580 12GB?

See all results for Intel Arc B580 12GB See all hardware for Pixtral 12B

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