# Can I run Llama 3.1 8B on an RTX 3080? | What Can I Run AI?

> Yes. Llama 3.1 8B at Q4_K_M needs about 6.6 GB; RTX 3080 has 10.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-3080-10gb/

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**Yes.** Llama 3.1 8B at Q4\_K\_M needs about 6.6 GB, and the GeForce RTX 3080 10GB has 10.0 GB usable at a \~4k context.

# Can I run Llama 3.1 8B on an RTX 3080?

Llama 3.1 8B at Q4\_K\_M needs about 6.6 GB, and the GeForce RTX 3080 10GB has 10.0 GB usable. That figure is 10 GB of VRAM on the spec sheet, of which 10.0 GB is usable for weights and cache, at 760 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

Q4\_K\_M Fits with headroom

Memory needed

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

Usable VRAM

10.0 GB of 10 GB VRAM

Est. tokens/sec

94–133 estimate

[Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/) · [GeForce RTX 3080 10GB](https://whatcanirunai.vercel.app/hardware/rtx-3080-10gb/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3080-10gb/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 10.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 6.6 GB          | Q4Fits        |
| Q8\_0    | 10.8 GB         | Q8Won’t fit   |
| FP16     | 19.5 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **94–133 tokens/sec** at Q4\_K\_M. 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 Q4\_K\_M, this machine has headroom out to about **16k 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 RTX 3080 at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.5 GB          | 6.6 GB            | Fits      |
| 8k      | 1.1 GB          | 7.1 GB            | Fits      |
| 16k     | 2.1 GB          | 8.2 GB            | Fits      |
| 32k     | 4.3 GB          | 10.4 GB           | Won’t fit |
| 64k     | 8.6 GB          | 14.7 GB           | Won’t fit |
| 128k    | 17.2 GB         | 23.2 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

* Q8\_0 needs 10.8 GB and does not fit here. The [GeForce RTX 2080 Ti](https://whatcanirunai.vercel.app/hardware/rtx-2080-ti/) has 11.0 GB usable, enough for Q8.
* 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 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
* [Llama 3.1 8B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-5070-ti/)Fits

## Other models on an RTX 3080

* [Qwen3-32B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3080-10gb/)Won’t fit
* [Qwen3-30B-A3B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3080-10gb/)Won’t fit
* [Qwen3.8-27B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-3080-10gb/)Won’t fit
* [Gemma 3 27B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-3080-10gb/)Won’t fit
* [Gemma 4 31B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3080-10gb/)Won’t fit
* [gpt-oss-20b on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3080-10gb/)Won’t fit

## Quick answers

Can I run Llama 3.1 8B on an RTX 3080?

Yes. Llama 3.1 8B at Q4\_K\_M needs about 6.6 GB, and the GeForce RTX 3080 10GB has 10.0 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 RTX 3080?

Our estimate is 94–133 tokens per second at Q4\_K\_M. It is not a measured benchmark.

What context length can I use?

At Q4\_K\_M, our KV estimate supports up to about 16k 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/