# Can I run Llama 3.3 70B on an Arc B580? | What Can I Run AI?

> No. Llama 3.3 70B needs about 50.2 GB at Q4_K_M, and Arc B580 has 12.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/arc-b580/

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**No.** Llama 3.3 70B at Q4\_K\_M needs about 50.2 GB, and the Intel Arc B580 has 12.0 GB usable at a \~4k context.

# Can I run Llama 3.3 70B on an Arc B580?

Llama 3.3 70B at Q4\_K\_M needs about 50.2 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 flagship 70B instruct model, the usual target for a high-end local chat box. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

None of Q4, Q8 or FP16 needs 50.2 GB

Memory needed

50.2 GB at Q4\_K\_M, \~4k context

Usable VRAM

12.0 GB of 12 GB VRAM

Est. tokens/sec

n/a does not fit

[Llama-3.3-70B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.3-70b-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 | 50.2 GB         | Q4Won’t fit   |
| Q8\_0    | 87.2 GB         | Q8Won’t fit   |
| FP16     | 163.3 GB        | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: Llama 3.3 70B does not fit in the 12.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.

## Context headroom

Even at a short \~4k context, the weights do not fit. Extending context would only increase the gap.

__Context headroom for Llama 3.3 70B on an Arc B580 at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 1.3 GB          | 50.2 GB           | Won’t fit |
| 8k      | 2.7 GB          | 51.5 GB           | Won’t fit |
| 16k     | 5.4 GB          | 54.2 GB           | Won’t fit |
| 32k     | 10.7 GB         | 59.6 GB           | Won’t fit |
| 64k     | 21.5 GB         | 70.3 GB           | Won’t fit |
| 128k    | 42.9 GB         | 91.8 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 14B](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-14b/arc-b580/) instead (11.3 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* To hold Llama 3.3 70B at Q4, you need about 50.2 GB. The [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/) has 96.0 GB usable ($6699 Apple list price, 14-inch M5 Max 18C CPU/40C GPU, 128GB/2TB).
* CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/llama/) and the [Llama-3.3-70B-Instruct model page](https://whatcanirunai.vercel.app/models/llama-3.3-70b-instruct/).

## Other hardware for Llama 3.3 70B

* [Llama 3.3 70B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-3060-12gb/)Won’t fit
* [Llama 3.3 70B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-3070-ti/)Won’t fit
* [Llama 3.3 70B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-3080-10gb/)Won’t fit
* [Llama 3.3 70B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-3090/)Won’t fit
* [Llama 3.3 70B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-4060-ti-16gb/)Won’t fit
* [Llama 3.3 70B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-4070-ti-super/)Won’t fit

## 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 3.3 70B on an Arc B580?

No. Llama 3.3 70B at Q4\_K\_M needs about 50.2 GB, and the Intel Arc B580 has 12.0 GB usable.

How much VRAM does Llama 3.3 70B need?

About 50.2 GB at Q4\_K\_M, 87.2 GB at Q8\_0 and 163.3 GB at FP16, estimated for a \~4k context.

How fast is Llama 3.3 70B on an Arc B580?

We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.

What context length can I use?

The weights already do not fit, so longer context is not the limiting factor.

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