# Can I run R1 Distill 32B on an RTX 4090? | What Can I Run AI

> Yes, but it is tight. R1 Distill 32B at Q4_K_M needs about 23.8 GB; RTX 4090 has 24.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-4090/

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**Yes, but it is tight.** R1 Distill 32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 4090 has 24.0 GB usable at a \~4k context.

# Can I run R1 Distill 32B on an RTX 4090?

R1 Distill 32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 4090 has 24.0 GB usable. That figure is 24 GB of VRAM on the spec sheet, of which 24.0 GB is usable for weights and cache, at 1008 GB/s memory bandwidth. A 32B DeepSeek-R1 distill on Qwen. Memory need matches Qwen3-32B; the weights are distilled from DeepSeek-R1\. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

Q4\_K\_M Fits, short context only

Memory needed

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

Usable VRAM

24.0 GB of 24 GB VRAM

Est. tokens/sec

30–43 estimate

[DeepSeek-R1-Distill-Qwen-32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/) · [GeForce RTX 4090](https://whatcanirunai.vercel.app/hardware/rtx-4090/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-4090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 24.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 23.8 GB         | Q4Tight       |
| Q8\_0    | 41.0 GB         | Q8Won’t fit   |
| FP16     | 76.4 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **30–43 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 **4k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for R1 Distill 32B on an RTX 4090 at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 1.1 GB          | 23.8 GB           | Tight     |
| 8k      | 2.1 GB          | 24.9 GB           | Won’t fit |
| 16k     | 4.3 GB          | 27.1 GB           | Won’t fit |
| 32k     | 8.6 GB          | 31.4 GB           | Won’t fit |
| 64k     | 17.2 GB         | 39.9 GB           | Won’t fit |
| 128k    | 34.4 GB         | 57.1 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 [Qwen3-32B](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4090/) instead (23.8 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* Q8\_0 needs 41.0 GB and does not fit here. The [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) has 48.0 GB usable, enough for Q8.
* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/deepseek-r1/) and the [DeepSeek-R1-Distill-Qwen-32B model page](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/).

## Other hardware for R1 Distill 32B

* [R1 Distill 32B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-3090/)Tight
* [R1 Distill 32B on an RTX 5080](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-5080/)Won’t fit
* [R1 Distill 32B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-5090/)Fits
* [R1 Distill 32B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/mac-mini-m6-24/)Won’t fit
* [R1 Distill 32B on an RX 7900 XTX](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rx-7900-xtx/)Tight

## Other models on an RTX 4090

* [Qwen3-32B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4090/)Tight
* [Qwen3-30B-A3B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4090/)Tight
* [Qwen3.8-27B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-4090/)Fits
* [Gemma 3 27B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-4090/)Fits
* [Gemma 4 31B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-4090/)Tight
* [gpt-oss-20b on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4090/)Fits

## Quick answers

Can I run R1 Distill 32B on an RTX 4090?

Yes, but it is tight. R1 Distill 32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 4090 has 24.0 GB usable.

How much VRAM does R1 Distill 32B need?

About 23.8 GB at Q4\_K\_M, 41.0 GB at Q8\_0 and 76.4 GB at FP16, estimated for a \~4k context.

How fast is R1 Distill 32B on an RTX 4090?

Our estimate is 30–43 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 4k tokens before the cache pushes the total over usable memory.

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