# Can I run Llama 3.2 3B on an Arc B580? | What Can I Run AI?

> Yes. Llama 3.2 3B at FP16 needs about 8.4 GB; Arc B580 has 12.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/arc-b580/

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**Yes.** Llama 3.2 3B at FP16 needs about 8.4 GB, and the Intel Arc B580 has 12.0 GB usable at a \~4k context.

# Can I run Llama 3.2 3B on an Arc B580?

Llama 3.2 3B at FP16 needs about 8.4 GB, and the Intel Arc B580 has 12.0 GB usable. That figure is 12 GB of VRAM on the spec sheet, of which 12.0 GB is usable for weights and cache, at 456 GB/s memory bandwidth. A tiny Meta Llama, aimed at phones and low-VRAM cards. Quality is limited; it is the model you pick when memory is tight. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

FP16 Fits with headroom

Memory needed

8.4 GB at FP16, \~4k context

Usable VRAM

12.0 GB of 12 GB VRAM

Est. tokens/sec

43–60 estimate

[Llama-3.2-3B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/) · [Intel Arc B580](https://whatcanirunai.vercel.app/hardware/arc-b580/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/arc-b580/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 12.0 GB | Verdict  |
| -------- | --------------- | -------- |
| Q4\_K\_M | 3.2 GB          | Q4Fits   |
| Q8\_0    | 4.9 GB          | Q8Fits   |
| FP16     | 8.4 GB          | FP16Fits |

## Estimated tokens per second

Estimate: **43–60 tokens/sec** at FP16\. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from [How we calculate](https://whatcanirunai.vercel.app/method/).

## Context headroom

At FP16, this machine has headroom out to about **32k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Llama 3.2 3B on an Arc B580 at FP16__
| Context | KV cache (FP16) | Total at FP16 | Verdict   |
| ------- | --------------- | ------------- | --------- |
| 4k      | 0.5 GB          | 8.4 GB        | Fits      |
| 8k      | 0.9 GB          | 8.9 GB        | Fits      |
| 16k     | 1.9 GB          | 9.8 GB        | Fits      |
| 32k     | 3.8 GB          | 11.7 GB       | Tight     |
| 64k     | 7.5 GB          | 15.4 GB       | Won’t fit |
| 128k    | 15.0 GB         | 23.0 GB       | Won’t fit |

From the model’s config. Try other sizes in the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see the [context length guide](https://whatcanirunai.vercel.app/context-length/).

## What to do instead

* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/llama/) and the [Llama-3.2-3B-Instruct model page](https://whatcanirunai.vercel.app/models/llama-3.2-3b-instruct/).

## Other hardware for Llama 3.2 3B

* [Llama 3.2 3B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/rtx-3060-12gb/)Fits
* [Llama 3.2 3B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/rtx-3070-ti/)Fits
* [Llama 3.2 3B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/rtx-4060-ti-16gb/)Fits
* [Llama 3.2 3B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/rtx-4090/)Fits
* [Llama 3.2 3B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/mac-mini-m6-16/)Fits

## Other models on an Arc B580

* [Qwen3-32B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/arc-b580/)Won’t fit
* [Qwen3-30B-A3B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/arc-b580/)Won’t fit
* [Qwen3.8-27B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/arc-b580/)Won’t fit
* [Gemma 3 27B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/arc-b580/)Won’t fit
* [Gemma 4 31B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/arc-b580/)Won’t fit
* [gpt-oss-20b on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/arc-b580/)Won’t fit

## Quick answers

Can I run Llama 3.2 3B on an Arc B580?

Yes. Llama 3.2 3B at FP16 needs about 8.4 GB, and the Intel Arc B580 has 12.0 GB usable.

How much VRAM does Llama 3.2 3B need?

About 3.2 GB at Q4\_K\_M, 4.9 GB at Q8\_0 and 8.4 GB at FP16, estimated for a \~4k context.

How fast is Llama 3.2 3B on an Arc B580?

Our estimate is 43–60 tokens per second at FP16\. It is not a measured benchmark.

What context length can I use?

At FP16, our KV estimate supports up to about 32k tokens before the cache pushes the total over usable memory.

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Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/