# Can I run Llama 3.3 70B on an RTX 3070 Ti? | What Can I Run?

> No. Llama 3.3 70B needs about 50.2 GB at Q4_K_M, and RTX 3070 Ti has 8.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-3070-ti/

[Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Llama 3.3 70B](https://whatcanirunai.vercel.app/models/llama-3.3-70b-instruct/) / Llama 3.3 70B on an RTX 3070 Ti

**No.** Llama 3.3 70B at Q4\_K\_M needs about 50.2 GB, and the GeForce RTX 3070 Ti has 8.0 GB usable at a \~4k context.

# Can I run Llama 3.3 70B on an RTX 3070 Ti?

Llama 3.3 70B at Q4\_K\_M needs about 50.2 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 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

8.0 GB of 8 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/) · [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 | 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No speed estimate: Llama 3.3 70B does not fit in the 8.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 RTX 3070 Ti 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 [Qwen3-8B](https://whatcanirunai.vercel.app/can-i-run/qwen3-8b/rtx-3070-ti/) instead (6.7 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 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
* [Llama 3.3 70B on an RTX 4080 Super](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-4080-super/)Won’t fit

## 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 3.3 70B on an RTX 3070 Ti?

No. Llama 3.3 70B at Q4\_K\_M needs about 50.2 GB, and the GeForce RTX 3070 Ti has 8.0 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 RTX 3070 Ti?

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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