# Can I run Llama 3.1 8B on an RTX 5070 Ti? | What Can I Run?

> Yes. Llama 3.1 8B at Q8_0 needs about 10.8 GB; RTX 5070 Ti has 16.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/llama-3.1-8b-instruct/rtx-5070-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 5070 Ti

**Yes.** Llama 3.1 8B at Q8\_0 needs about 10.8 GB, and the GeForce RTX 5070 Ti has 16.0 GB usable at a \~4k context.

# Can I run Llama 3.1 8B on an RTX 5070 Ti?

Llama 3.1 8B at Q8\_0 needs about 10.8 GB, and the GeForce RTX 5070 Ti has 16.0 GB usable. That figure is 16 GB of VRAM on the spec sheet, of which 16.0 GB is usable for weights and cache, at 896 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 with headroom

Memory needed

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

Usable VRAM

16.0 GB of 16 GB VRAM

Est. tokens/sec

63–89 estimate

[Llama-3.1-8B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.1-8b-instruct/) · [GeForce RTX 5070 Ti](https://whatcanirunai.vercel.app/hardware/rtx-5070-ti/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-5070-ti/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 16.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 6.6 GB          | Q4Fits        |
| Q8\_0    | 10.8 GB         | Q8Fits        |
| FP16     | 19.5 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **63–89 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 **32k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Llama 3.1 8B on an RTX 5070 Ti at Q8\_0__
| Context | KV cache (FP16) | Total at Q8\_0 | Verdict   |
| ------- | --------------- | -------------- | --------- |
| 4k      | 0.5 GB          | 10.8 GB        | Fits      |
| 8k      | 1.1 GB          | 11.3 GB        | Fits      |
| 16k     | 2.1 GB          | 12.4 GB        | Fits      |
| 32k     | 4.3 GB          | 14.6 GB        | Tight     |
| 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

* 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 RTX 5070 Ti

* [Qwen3-32B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-5070-ti/)Won’t fit
* [Qwen3-30B-A3B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-5070-ti/)Won’t fit
* [Qwen3.8-27B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-5070-ti/)Won’t fit
* [Gemma 3 27B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-5070-ti/)Won’t fit
* [Gemma 4 31B on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-5070-ti/)Won’t fit
* [gpt-oss-20b on an RTX 5070 Ti](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-5070-ti/)Tight

## Quick answers

Can I run Llama 3.1 8B on an RTX 5070 Ti?

Yes. Llama 3.1 8B at Q8\_0 needs about 10.8 GB, and the GeForce RTX 5070 Ti has 16.0 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 5070 Ti?

Our estimate is 63–89 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 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/