# Can I run Llama 3.1 8B on an Arc B580? | What Can I Run AI?

> Yes, but it is tight. Llama 3.1 8B at Q8_0 needs about 10.8 GB; Arc B580 has 12.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/arc-b580/

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**Yes, but it is tight.** Llama 3.1 8B at Q8\_0 needs about 10.8 GB, and the Intel Arc B580 has 12.0 GB usable at a \~4k context.

# Can I run Llama 3.1 8B on an Arc B580?

Llama 3.1 8B at Q8\_0 needs about 10.8 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. Meta’s widely used 8B instruct model, the default small local chat model for a few years running. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

Q8\_0 Fits, short context only

Memory needed

10.8 GB at Q8\_0, \~4k context

Usable VRAM

12.0 GB of 12 GB VRAM

Est. tokens/sec

32–45 estimate

[Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-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 | 6.6 GB          | Q4Fits        |
| Q8\_0    | 10.8 GB         | Q8Tight       |
| FP16     | 19.5 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **32–45 tokens/sec** at Q8\_0\. 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 Q8\_0, this machine has headroom out to about **8k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Llama 3.1 8B on an Arc B580 at Q8\_0__
| Context | KV cache (FP16) | Total at Q8\_0 | Verdict   |
| ------- | --------------- | -------------- | --------- |
| 4k      | 0.5 GB          | 10.8 GB        | Tight     |
| 8k      | 1.1 GB          | 11.3 GB        | Tight     |
| 16k     | 2.1 GB          | 12.4 GB        | Won’t fit |
| 32k     | 4.3 GB          | 14.6 GB        | Won’t fit |
| 64k     | 8.6 GB          | 18.9 GB        | Won’t fit |
| 128k    | 17.2 GB         | 27.5 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

* Run [R1 Distill 7B](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-7b/arc-b580/) instead (6.3 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* Keep context short, close other GPU work, and avoid a higher quant. The [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) shows how the KV cache grows.
* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/llama/) and the [Llama-3.1-8B-Instruct model page](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/).

## Other hardware for Llama 3.1 8B

* [Llama 3.1 8B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-3060-12gb/)Tight
* [Llama 3.1 8B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-3070-ti/)Fits
* [Llama 3.1 8B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-3080-10gb/)Fits
* [Llama 3.1 8B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-4060-ti-16gb/)Fits
* [Llama 3.1 8B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-4070-ti-super/)Fits
* [Llama 3.1 8B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-4090/)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 3.1 8B on an Arc B580?

Yes, but it is tight. Llama 3.1 8B at Q8\_0 needs about 10.8 GB, and the Intel Arc B580 has 12.0 GB usable.

How much VRAM does Llama 3.1 8B need?

About 6.6 GB at Q4\_K\_M, 10.8 GB at Q8\_0 and 19.5 GB at FP16, estimated for a \~4k context.

How fast is Llama 3.1 8B on an Arc B580?

Our estimate is 32–45 tokens per second at Q8\_0\. It is not a measured benchmark.

What context length can I use?

At Q8\_0, our KV estimate supports up to about 8k tokens before the cache pushes the total over usable memory.

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