# Can I run Llama 3.1 8B on an RTX 3070 Ti? | What Can I Run?

> Yes. Llama 3.1 8B at Q4_K_M needs about 6.6 GB; RTX 3070 Ti has 8.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-3070-ti/

[Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Llama 3.1 8B](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/) / Llama 3.1 8B on an RTX 3070 Ti

**Yes.** Llama 3.1 8B at Q4\_K\_M needs about 6.6 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable at a \~4k context.

# Can I run Llama 3.1 8B on an RTX 3070 Ti?

Llama 3.1 8B at Q4\_K\_M needs about 6.6 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable. That figure is 8 GB of VRAM on the spec sheet, of which 8.0 GB is usable for weights and cache, at 608 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

8.0 GB of 8 GB VRAM

Est. tokens/sec

75–106 estimate

[Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/) · [GeForce RTX 3070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-3070-ti/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3070-ti/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 8.0 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: **75–106 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 **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 RTX 3070 Ti 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            | Won’t fit |
| 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 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
* [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 3070 Ti

* [Qwen3-32B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3070-ti/)Won’t fit
* [Qwen3-30B-A3B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3070-ti/)Won’t fit
* [Qwen3.8-27B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-3070-ti/)Won’t fit
* [Gemma 3 27B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-3070-ti/)Won’t fit
* [Gemma 4 31B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3070-ti/)Won’t fit
* [gpt-oss-20b on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3070-ti/)Won’t fit

## Quick answers

Can I run Llama 3.1 8B on an RTX 3070 Ti?

Yes. Llama 3.1 8B at Q4\_K\_M needs about 6.6 GB, and the GeForce RTX 3070 Ti has 8.0 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 3070 Ti?

Our estimate is 75–106 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 8k 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/